Final Year Dissertation 2003€¦ · A ck nowle dgem e nt s k:nah t o t d ek w l Il i uor l aui n c...

Final Year Dissertation 2003

T H E EF F E C T O F C L I M AT E C H A N G E O N STR E A M F L O W

A s tudy on the effe c t of climate c ha nge on strea mflow in south-w es t W es te rn Aus tralia

By: Ceci li a Har vey

03 Novem ber 2003

Abs tr ac t:

I II

A bs tr ac t

Runof f processes ar e a hi ghly complex funct i on of rai nf all and catchment char act er ist ics.

Runof f is af f ected by rai nf al l featur es, such as, tot al rainf al l, intensi ty and durat ion. A shor t

r ai nf al l event of hi gh intensit y wil l produce mor e runof f than a longer rai nf al l event wi th the

sam e tot al rainfall . The geol ogi cal and topogr aphic char acter isti cs of catchm ent s ar e highl y

signi fi cant in the processes of runof f gener at ion. Each catchment has a par ti cul ar response to

each rai nf al l event .

As many of these pr opert i es are both spat ial ly and temporall y var iable complex model s are

r equi red to accur at ely si mulate runof f gener at ion in the cat chm ent. Model s such as these

r equi re hi ghl y speci fi c and det ail ed data of bot h the rainfal l and the catchm ent char acteri sti cs.

F ew cat chm ent s have such data readil y avail abl e and it is expensi ve to obtain through field

m easurem ents.

I n the mid 1970s the sout h- west of West er n Austr ali a exper ienced a clear shif t in the cli mat ic

charact eri st i cs of the regi on. Thi s shi ft was manif ested in a decrease in bot h the mean and

standar d devi at ion winter rai nf all . Cli mate pr edi ct ions indi cat e that we may expect a fur ther

sim il ar change in r ainfal l patt erns over the next 30yrs.

T hi s di ssert ati on expl or es the hypot hesis that a si mple regr ession analysis may be cali br at ed

usi ng the cl i mate vari abi li ty of the past to predict li kel y consequences of the pr edi ct ed future

r ai nf al l changes on runof f.

Ack no wle dg eme nts

I V

A ck nowle dgem e nt s

I would li ke to t hank al l t hose who assisted m e thi s year, i n par ti cul ar I woul d l ike t o thank:

My super vi sor , Jörg Im ber ger for the or iginal idea and his encour agement and honesty

t hr oughout t he year .

Gar y McCal l from the Bur eau of Met eor ol ogy and Rosemary Lear ch fr om the Wat er and

River s Com mi ssi on f or their assi st ance wi th requi si ti on of data.

Michell e De Souza f or her conti nual pat ience and supply of ar ti cl es.

T he final year cl ass of 2003, who have been such gr eat fri ends and made final year a bl ast.

T hey have pr ovi ded suppor t when needed, and not to ment i on comi c rel ief.

On a per sonal not e, thanks to my fri ends and fam i ly. My extended fam il y for thei r support and

encouragem ent , in part icular to my uncl e Dr . I, who had the pat ience to edi t my dr af t . To my

sisters for their suppor t and my brot her for his st or ies. And to my parents, who bot h know

preci sel y how I woul d have coped wit hout them bei ng them thi s year. Thanks to Annali sa,

Jul ie, Lei th and Jo for most im por tantl y making me laugh at mysel f.

F inal ly I would like to dedicat e thi s thesi s to Louise Bauer who had no int er est in engineer ing

but who had the most amazing abi li ty to make ever ybody laugh, smi le and be happy. I’ l l mi ss

you L ou.

V

Table of C ont ents

1 I NT RO DUCTI ON 1

2 L IT ERAT URE REVI EW 3

2 .1 R un of f G en era tion 3 2 .1 .1 S tr eamf low 3 2 .1 .2 P ro cess es of Ru no ff Gener atio n 4 2 .1 .3 Con tr ib u tion s to str eamf low after a r ainf all even t 6 2 .1 .4 Mod els 7 2 .1 .5 H yd ro lo g ic Mo dels 8 2 .1 .6 A nteced ent Mo is tu re Co nd ition s 8 2 .1 .7 Eff ect o f Veg etatio n 9

2 .2 R ainf all in s ou th -w est West ern A us tra lia 1 02 .2 .1 G lo bal Climate Ch an g e 1 02 .2 .2 Climate Ch an g e in A u stralia 1 02 .2 .3 The Clim ate o f so uth -w es t W es ter n Au s tr alia 1 12 .2 .4 Chang es to r ain fall in th e so uth -w es t r ainf all 1 22 .2 .5 Eff ect o f climate ch an ge on s ou th- wes t rain f all 1 4

3 SIT E DESCRIPT IO N 1 7

3 .1 6 12 – C o llie Ba sin C at ch men t 1 93 .1 .1 S ite Ch aracteristics o f the Collie River Bas in 1 93 .1 .2 S tr eamf low s ites 2 03 .1 .3 Rainf all s ites 2 1

3 .2 6 03 – D enmark C oa st Ba sin 2 13 .2 .1 S ite Ch aracteristics o f the D en m ar k Coast Basin 2 23 .2 .2 S tr eamf low s ites 2 33 .2 .3 Rainf all s ites 2 3

3 .3 6 05 – Fran kla nd R iv er Ba s in 2 33 .3 .1 S ite Ch aracteristics o f the F ran klan d Riv er Basin 2 43 .3 .2 S tr eamf low s ites 2 43 .3 .3 Rainf all s ites 2 4

3 .4 6 06 – S h an no n R iv er Ba sin 2 53 .4 .1 S ite Ch aracteristics o f the S han no n Riv er Basin 2 53 .4 .2 S tr eamf low s ites 2 63 .4 .3 Rainf all s ites 2 6

3 .5 6 07 – Wa rren River Bas in 2 63 .5 .1 S ite Ch aracteristics o f the W ar r en River Bas in 2 63 .5 .2 S tr eamf low s ites 2 73 .5 .3 Rainf all s ites 2 8

3 .6 6 10 – Bu ss elt on C oa s t Ba s in 2 93 .6 .1 S ite Ch aracteristics o f the Bus s elto n Coast Basin 2 93 .6 .2 S tr eamf low s ites 3 03 .6 .3 Rainf all s ites 3 0

VI

4 M ODEL DEVELO PMENT AND APPLI CATI O N 3 1

4 .1 M od el D evelo p ment 3 14 .1 .1 Mem or y 3 24 .1 .2 Lin ear Mod el 3 34 .1 .3 U se o f Multip le r ain fall statio n s 3 44 .1 .4 Trans fo r matio n of v ariab les 3 54 .1 .5 Multiple Reg r es sion 3 74 .1 .6 S easo nal Mod ificatio n 3 94 .1 .7 N on -lin ear F its 4 3

4 .2 The mod el 4 64 .2 .1 Calib ratio n o f th e m od el 4 7

4 .3 A pp lica t io n o f th e mod el 4 74 .3 .1 The eff ect o n s tr eam flow of clim ate chang e in th e s ou th - west 4 84 .3 .2 The eff ect o f climate ch ang e on in flo ws to P er th dams 4 9

4 .4 S umma ry of t h e mo del 5 0

5 ADVANTAG ES AND LI MI T AT IO NS OF T HE MO DEL 5 1

5 .1 S pa tial an d Tempo ra l V ariab ilit y 5 15 .1 .1 F utur e P ro jection s 5 2

5 .2 I mp lica t io ns fo r pla nn in g 5 35 .2 .1 Climate ch an g e or clim ate v ar iab ility 5 35 .2 .2 W ater P lan nin g 5 3

6 CONCL USI ONS 5 5

7 REF ERENCES 5 6

8 APPENDI CES 6 1

8 .1 A pp en dix A 6 1

8 .2 A pp en dix B 7 0

VII

Lis t of Ta ble sT ab le 3 -1 : Th e AWRC n um bers an d n am es o f the dr aina g e ba s in s in th e s ou th -west o f Wes ter n Au s tr alia (WRC,2 00 3) 1 8T ab le 3 -2 : D es cription o f the s tream flo w sta tion s within the Co llie River Bas in 2 0T ab le 3 -3 : Descr ip tio n of th e ra infall s ta tio ns with in th e Co llie River Bas in 2 1T ab le 3 -4 : D es cription o f the s tream flo w sta tion s within the D enm ar k Coa st Ba sin 2 3T ab le 3 -5 : Descrip tion of the ra in fall sta tion s within the Denm ar k Coa s t Ba sin 2 3T ab le 3 -6 : D es cription o f the s tream flo w sta tion with in th e Fr a nkla n d River Bas in 2 4T ab le 3 -7 : Descr ip tio n of th e ra infall s ta tio n with in th e Fra nklan d River Ba sin 2 4T ab le 3 -8 : D es cription o f the s tream flo w sta tion s within the S h an no n River Ba sin 2 6T ab le 3 -9 : Descr ip tio n of th e ra infall s ta tio n with in th e S ha nn o n River Bas in 2 6T ab le 3 -1 0: Des cr ip tio n of th e s tr ea mflow s tatio n within the Wa rr en River Bas in 2 7T ab le 3 -1 1: D es cription o f the r a in fa ll station with in th e Wa rr en River Bas in 2 8T ab le 3 -1 2: Des cr iptio n o f th e s tr ea m flow statio n with in the Bu ss elton Co as t Bas in 3 0T ab le 3 -1 3: D es cription o f the r a in fa ll station with in th e Bu ss elton Co as t Bas in 3 0T ab le 4 -1 : Th e a utor egr es sio ns o f var io u s sta tion s in th e s ou th - west 3 2T ab le 4 -2 : Co rr ela tio n co efficien ts to m ea su r e th e s ucces s of th e lin ea r mo d el. 3 4T ab le 4 -3 : co rr ela tio n co efficien ts for th e two cas es of tr an sfo rm in g the a n nu al ra in fa ll 3 6T ab le 4 -4 : co rr ela tio n co efficien ts for mu ltiple reg ress io n an alysis 3 8T ab le 4 -5 : Resu lts o f the s eas on a l mo dification o f the r a in fa ll da ta 4 3T ab le 4 -6 : Th e cor relatio n coefficien ts fo r th e two q ua dr a tic mo d els, ca se A an d cas e B 4 5T ab le 4 -7 : Th e equ ation o f the m o del fo r the nine ca tchm ents 4 6T ab le 4 -8 : Ch an g es to m ea n an n ua l s tr ea mflow a fter a 3 0% r edu ctio n in win ter r ainfa ll d ue to clim ate cha ng e. 4 8

VII I

Table of Figure s

Fig ur e 2 -1 : Tr en d s in winter ra in fall in Ma njimu p, Wester n Aus tr a lia ( so ur ce Ba te, 2 00 2) .______________________ 1 3Fig ur e 2 -2 : Th e r elative cha n ges to mean an nu a l ra in fall in th e m id 1 9 70 s at fo ur va riou s loca tion s in th e s ou th - wes t of Western Aus tra lia , Pemb erton , Per th , Man jim up a n d Alb an y. _______________________________________ 1 4Fig ur e 2 -3 : Ra ng es of aver ag e s ea s on al a n d an n ua l ra infall cha ng e ( %) fo r ar o un d 2 03 0 an d 2 07 0 r elative to 1 99 0. T h e co lou red b ar s s ho w ra n ges o f ch an g e fo r a reas with co rr es p on din g co lo u rs in the m a ps . Ran ges a re n o tg iven fo r ar eas with s ea s on ally lo w r ainfall b eca us e per centa ge cha n ges in ra in fall can no t b e as reliab lycalcu la ted o r a pp lied in su ch r egion s ________________________________________________________________ 1 6Fig ur e 3 -1 : Left: T he ma jo r d ra in a ge b as ins o f Wes ter n Au s tr alia , Rig h t: T he ma jo r d ra in a ge b a sins o f the so uth- wes t dr a in ag e d ivis ion ( WRC, 20 0 3) _________________________________________________________________ 1 8Fig ur e 3 -2 : A ma p o f the Collie River Ba s in ___________________________________________________________ 1 9Fig ur e 3 -3 : A ma p o f the D en m ar k Coa st Ba sin ________________________________________________________ 2 1Fig ur e 3 -4 : A ma p o f the Fra n klan d River Ba sin _______________________________________________________ 2 3Fig ur e 3 -5 : A ma p o f the S ha n no n River Ba sin_________________________________________________________ 2 5Fig ur e 3 -6 : A ma p o f the War r en River Ba s in __________________________________________________________ 2 6Fig ur e 3 -7 : A ma p o f the Bus s elto n Coa st Ba sin ________________________________________________________ 2 9Fig ur e 4 -1 : Time series fo r a nn ua l r ainfa ll a n d an nu a l str ea mflo w o ver their perio ds o f r ecor d s. _________________ 3 2Fig ur e 4 -2 : Ra w s tr ea m flow d a ta a n d th e s im ula ted str ea mflow d ata u sin g th e lin ea r m od el. ____________________ 3 3Fig ur e 4 -3 : A sch em atic repr esenta tion o f the sq ua re tr an s fo rm ation _______________________________________ 3 5Fig ur e 4 -4 : A Sch em atic repr esenta tion o f a lo g tr an s fo rm _______________________________________________ 3 6Fig ur e 4 -5 : An nu a l str ea mflo w ver s us r ain fa ll, h ig hligh tin g year s with s im ila r ra infalls bu t differen t strea mflo w. ____ 3 9Fig ur e 4 -6 : Mo nth ly r a in fa ll an d s tr ea mflow va lu es fo r th e yea rs of 1 9 82 to 1 98 3 a nd 1 98 7 to 1 98 8.______________ 4 1Fig ur e 4 -7 : Linea r mo d el a pp lied to th e s ea so n ally m o dified da ta __________________________________________ 4 3Fig ur e 4 -8 : Simu lated stream flo w d ata fo r the ca tchm ent o f 6 12 00 2 . _______________________________________ 4 5Fig ur e 4 -9 : An nu a l str ea mflo w for ma jo r s ur fa ce wa ter s ou r ces fo r Per th. ( So u rce: Wa ter Cor po r atio n) ___________ 4 9Fig ur e 8 -1 : La nd us es fo r th e D en m ar k Co a st Ba sin ____________________________________________________ 7 0Fig ur e 8 -2 : La nd us es in the Fr an kla nd River Bas in ____________________________________________________ 7 1Fig ur e 8 -3 : La nd us es in the Sh an n on River Ba s in ______________________________________________________ 7 1Fig ur e 8 -4 : La nd us es fo r th e War r en River Ba s in ______________________________________________________ 7 2Fig ur e 8 -5 : La nd us es fo r th e Bus s elto n Coa st Ba sin ____________________________________________________ 7 2Fig ur e 8 -6 : La nd us es in th e Collie River Ba s in ________________________________________________________ 7 3

C ha pter 1: In tr od uc tio n

T he Effect o f Clima te Ch a ng e on Strea mflo w in th e S ou th - West Wester n Aus tr alia 1

1 Int roduc tionCivil isati on is pri m ar il y dependent on adequat e wat er supply. As the trend towar ds larger

popul at i on cent res and incr easi ng industr ial isat i on cont inues there wi ll be an increasi ng water

dem and for dr inki ng, sani tati on, irr i gati on, indust ry and power generati on wi ll incr ease

( Wi lson, E . 1990) .

I n West ern Aust rali a, conti nuing populati on gr owt h in Mandur ah and the Pert h met ropol it an

r egion is the maj or fact or in the gr owi ng demand for wat er . There has been a 20% incr ease in

t he dom est ic water use per capi t a of Pert h’ s wat er suppl y over the last 20 year s from 98 to

120kL . Thi s has occurr ed concur r entl y wit h a signif icant reduct ion in annual rai nf al l and

annual str eam fl ow. These changes have hei ght ened the awareness of water suppl y issues in

Western Aust r al ia and have hi ghl ight ed a need for com pr ehensi ve wat er managem ent plans

( Water Cor por at ion, 2003) . Hydr ologi cal resear ch ensures that new technol ogies for water

conservati on ar e devel oped and that there is a bett er underst andi ng of the im pl i cati ons of

cli mate change, incr eased urbani sati on and other fact or s aff ect ing changes in gr ound and

sur face water avail abi li t y (Wat er Cor porati on, 2003b) .

T he obj ect ive of thi s st udy is to devel op a quant it at ive rel ati onshi p bet ween rainfal l and

str eamf l ow t o assess t he ef fect that cl im at e change m ay have on str eam fl ow in t he fut ur e.

I t is cl ear from a num ber of research paper s t hat t he sout h- west of the state underwent a m arked

change in it s annual rai nfall in the mi d 1970s, in which the regi on recei ves on aver age 10% to

20% less annual rai nfall and has fewer rain days. Thi s decli ne in rainfal l as a resul t of cl im at e

change is si m il ar to t hat whi ch is pr oj ected to occur over the next 30 year s, t he year 2030. The

annual inf lows to the maj or sur f ace wat er sour ces for Pert h are experi enced a 50% decrease in

annual inf low from a decr ease in rai nfall of 10 to 20% over the sam e tim e per iod. Such

dramati c responses of runof f fr om cl i mate change il lust r at e the need for comprehensi ve

planning str ategi es and eff ecti ve model s to pr edi ct f ur t her changes to st ream fl ow.

T hi s st udy develops a si m pl e em pir ical model for which the only i nputs ar e summ er and winter

r ai nf al l and annual st reamf low. It appl ied a quadrati c fit to seasonal ly modi fi ed rai nf al l dat a. A

r educti on of wi nt er rainf al l of 30% acr oss the sout h- west wi l l resul t in a decr ease in the mean

C ha pter 1: In tr od uc tio n

T he Effect o f Clima te Ch a ng e on Strea mflo w in th e S ou th - West Wester n Aus tra lia 2

annual str eam fl ow of appr oxim at ely 50%. Wit h such sim pl e i nputs t he model i t is easy to appl y

t he m odel to many catchm ent s wi t h li m it ed data.

T hi s contr ast s wi th curr ent m odels t hat predict the change in str eam fl ow due to cl im ate change.

T hese more sophisti cat ed models have large dat a input requir ement s. One such model is

L AS CAM (Large-scale catchment model) which sim ul ates the hydr ol ogical pr ocesses at the

subcatchment scal e, befor e being aggr egat ed to yi el d the response of the enti re catchment . It

predi ct s the long-t erm im pact of land use and cl i mati c changes on the dai ly trends of str eam

f low and wat er qual i ty ( r epresented by salt , sedi ment s and nutr ient s) (CWR, undated) .

C ha pter 2: L ite ra tu r e Re v ie w

T he Effect o f Clima te Ch a ng e on Strea mflo w in th e S ou th - West Wester n Aus tr alia 3

2 Lit er at ure R e view2.1 R unoff Gener ation

T o st udy the pr ocesses of runof f generati on, land is di vided into unit s cal led cat chm ents. A

cat chment is defi ned rel ati ve to a specif ic locat ion, it encompasses all land which ult im at ely

drains to that part i cular poi nt (Hor nberger , G. et al , 1998) .

Runof f processes ar e com plex and are dependent on rai nf all and catchment char act er ist ics.

Runof f is af f ected by rai nf al l featur es, such as total rai nf all , it s i nt ensit y and i t s durat ion. Gi ven

t he sam e tot al rainf al l, a shor t rai nfall event of hi gh intensi ty wi ll pr oduce mor e runof f than a

l onger rai nf all event. The geol ogi cal , topographi c, soi l and vegetat ion charact eri st i cs of

cat chments ar e hi ghl y si gni fi cant in the pr ocesses of runoff generat ion. Each catchm ent has a

uni que response t o each rai nf al l event.

As many of these factors ar e bot h spati al ly and tem poral ly vari able, com plex model s are

r equi red to accur at ely si mulate runof f gener at ion in any par t icul ar catchment . These more

com pl ex model s requi re hi ghly specif i c and det ai l ed dat a for both rainfal l and cat chm ent

charact eri st i cs. There ar e few cat chm ents that have such dat a readi l y avail able and it is

expensi ve to obtain through f iel d measurements.

2.1.1 Streamflow

S tr eamf l ow is the pr im ar y com ponent of surf ace fl ow, int o whi ch all ot her for ms of surf ace

r unof f ult im ately cont ri but e (Chow et al . 1988) . On a global aver age, surf ace runof f com pri ses

38% of the rainfall total (Hornber ger , G. et al 1998). When rain fall s on the ground sur face, it

m ay be int er cepted by veget at ion and evapor ate, whi lst som e inf il tr ates int o the soi l and

per colat es downwards to the wat er table. The wat er tabl e is at the top of the zone of sat ur ati on,

where the por es of soi l or rock ar e sat ur at ed wi t h water . Wat er stor ed in the zone of sat ur ati on

i s known as groundwater (Fett er , 2001). Wat er may rem ai n in aquif er s and cont ri but e to

groundwater flow, or it may be drawn up by veget ati on and tr anspi re into the at m osphere. The

wat er remaini ng on the surf ace eit her evapor at es or coal esces int o str eam lets and runs as

sur face runof f into st ream channel s. Al l sur face waters exper ience evapor at ion (Wi lson, E.

1990) .



2.1.1.1 Components of streamflow

T he tim escal e of the conver si on of rainfall to st ream fl ow is dependent on the tr avel path of the

wat er . There ar e two com ponents to st ream fl ow, t hese ar e basefl ow and qui ckfl ow.

Baseflow

Basef low ref ers to wat er that percol ates int o the ground and is conveyed to the st ream channel

over long per iods of tim e, ther eby sust ai ni ng st r eamf low dur i ng per i ods wit hout rainf al l

( Maryland Depar tm ent of Nat ur al Resources, 2003) . It is al so known as “ dry- weather fl ow”

( Li nsley et al . 1982) . In som e cases it may take more than two year s for a gi ven accret ion to

groundwater to be di schar ged int o st r eams (L insl ey et al . 1982) . Though mor e recent studies

have dem onst r at ed that af ter a rai nf all event, gr oundwat er can be mobi li sed rapi dl y and for m a

signi fi cant contr ibuti on to str eam fl ow (McDonnel l , 1990)

Quickflow

Qui ck f l ow consists of the water t hat is tr anspor ted qui ckly to the st reams aft er a rai nf al l event,

and f or m s the seasonal component of str eamf l ow ( L insl ey et al . 1982) .

2.1.2 Processes of Runoff Generation

S tr eamf l ow compri ses of dir ect preci pit at ion int o str eam channels, overl and flow, shall ow

subsurf ace st or mf low and gr oundwat er fl ow ( Hor nberger , G. et al . 1998) .

2.1.2.1 Soil Infiltration Capacity

T he inst antaneous infi lt r at ion capaci ty is an inher ent proper ty of soi ls. The infi lt r at ion capaci ty

i s a measure of the maxi m um amount of wat er that can inf il tr ate int o the soil at that par ti cul ar

i nstant in ti me. It is dependent on the por osi ty and hydraul i c conduct ivi ty of t he soil . Inf il tr ati on

r at e is the rat e at which t he pr ecipi tati on is actual ly infi l tr at ing i nt o t he soil ( F et ter, 2001) .

P referenti al pathways ar e pat hways that provide the least resistance to flow. These may be root

channel s, wor m holes or som e ot her st ruct ur es that lower r esi st ance to water fl ow in the soi l.

2.1.2.2 Direct precipitation

T he cont ri but ion to st reamf low from dir ect rai nf all int o str eam channels is mini mal since the

sur face ar ea of t he channel syst em i s onl y a smal l percent age of the t ot al catchment ar ea.



2.1.2.3 Overland Flow

T here ar e two com ponents of over land fl ow:

- i nf il tr ati on excess over l and fl ow, ( also known and Hort oni an over land fl ow) and

- saturat ion overl and f low ( Chow et al , 1988) .

Discount ing dir ect preci pit at ion, overl and flow is the fastest rout e by whi ch water reaches the

str eam channel (Hor nberger, G. et al , 1998) .

F or ested cat chm ents have hi gh infi lt r at ion capaci ti es due to the pr esence of veget at i on. The

veget at i on pr events rainf al l fr om com pact ing the soil and pr ovi des roots that cause the soi l to

be porous and per meabl e. This is in contr ast to urban catchm ent s whi ch have low infi l tr at ion

capacit i es due to t he nat ur e of the ground sur face (Hor nberger, G. et al , 1998) .

Infiltration excess overland flow

I nf il tr ati on excess over l and fl ow occur s when rat e of rainfal l exceeds the inst ant aneous

i nf il tr ati on capaci t y. T his causes water to “pond” on t he gr ound sur face, and t hen over land fl ow

occur s (Hornber ger, G. et al , 1998) .

Hor toni an overl and flow dom inat es the r esponse t o r ai nf all and it shows increasi ng f l ood ri sk in

cat chments wher e the ground sur f ace has low perm eabil it y, or infi lt r at ion capaci ty. These ar e

t ypical l y ur ban or clear ed catchment s. The high inf il tr ati on capaci t y of forest ed cat chment s

m eans t hat Hort onian over land f l ow i s not si gnif i cant ( Hor nberger , G. et al , 1998) .

Saturation excess overland flow

S at ur at i on excess over land fl ow occur s when rainf al l has caused the water table to ri se unt i l it

i nt er sects the gr ound sur face. Areas pr one to sat ur at ion excess over land fl ow ar e those near

str eam channels or where gr oundwat er di schar ge occurs ( Hor nberger , G. et al 1998).

2.1.2.4 Shallow subsurface stormflow

S hall ow subsurf ace storm f low resul ts from l ocal sat ur at i on i n t he soil beneath the surf ace. Once

t he wat er inf il tr at es int o soil it cont inues to move downwar ds, due to the infl uence of gravit y.

As dept h bel ow the sur face incr eases, the hydr aul ic conducti vit y of the soi l decreases, result ing

i n local sat urati on. T he hydraul ic conductivit y is a measure of f low r esi st ance in t he soil (F et t er ,

2001) . Wat er moves from these ar eas towar ds st ream channel s by shal l ow subsur face

storm fl ow. This movement may be sl ow, and cont ri but e to basef low, or it may occur thr ough



preferenti al pathways, such as wor m hol es and root channel s, and thus be quit e rapid and hence

contr ibute t o qui ckf low (Hornber ger, G. et al 1998).

2.1.2.5 Groundwater flow

T hi s is the slowest of al l the pat hways t hr ough the cat chm ent and i t m ay lag pr eci pi t at ion for up

t o sever al year s. It for m s the lar gest component of basefl ow duri ng low flow per iods

( Hornber ger, G. et al 1998).

2.1.3 Contributions to streamflow after a rainfall event

T he flow pat hways that domi nate duri ng st or m events det erm ine the result i ng chem istr y of the

str eamf l ow both dur i ng and af ter the st or m (Bonnell , 1993) . Thi s knowl edge has been

com bi ned wit h the use of chem ical tr acers to det erm ine the sour ces of st r eamf low from wit hi n

t he cat chm ent . Many of the physi cal mechani sms that result in str eam fl ow occur in the one

cat chment, thei r dom inance depends on ant ecedent moistur e condi ti ons (El senbeer et al . ,

1994) , rai nf all int ensit y (McDonnell , 1990) , soi l depth vari abi li ty (Ross et al , 1994) , and

under lyi ng bedr ock topogr aphy (Bramm er and McDonnel l, 1996). The use of chemi cal tracer s

has been used t o det er mi ne the rel evant propor ti ons of the sour ces

L insl ey et al , 1972 hypot hesised that af ter a rai nfall event the basefl ow cont ri but ion onl y rises

sli ghtl y and the maj or it y of the discharge in st r eams is due to qui ckf low and subsur f ace runof f.

I f this were the case, the si gnature of a chem ical tr acer int roduced to the syst em should be

signi fi cantl y reduced due to di l ut ion dur ing a rainfall event . However , thi s is not the case and

whi lst str eam fl ow responds rapi dly to rai nf all , the chem ical si gnat ure is signi f icant ly dam ped,

i ndicat i ng that stor mf low com pr i sed mostl y of ‘ol d’ wat er (Ki rchner , 2003). That is,

cat chments ar e able to st or e wat er for weeks to months then rel ease it quickl y in response to

r ai nf al l inputs (McDonnel l, 1990). Abdul and Gil l ham, 1989 hypothesi sed that thi s

phenomenon coul d be expl ained by the concept of capil lar y fr i nge induced gr oundwat er

r idgi ng. Thi s concept resol ves the phenom ena of the constant chem ical si gnature, but does not

account for the flow experi enced aft er a rai nf al l event (McDonnel l, 1990) . Gi ven that the ‘old’

wat er is not being gener ated in the capil lar y zone near the str eam, it can be inferr ed that it is

t ravell i ng f r om areas away fr om the channel (McDonnel l, 1990) .

S oi l por osit y decreases exponent iall y wit h depth, whi ch means t hat less wat er can be st or ed per

vol um e of soi l deeper in the soi l pr ofi le. Discharge in most ri vers is st rongly domi nat ed by



out fl ow fr om a shal l ow gr oundwat er reservoi r exf i lt rati ng al ong t he si des and bases, not dur ing

dry per i ods, but al so dur ing rai nstor m event s (Wi tt enbur g, 1999).

2.1.4 Models

Hydrologic models pr ovide the fr am ework for deter mi ni ng relat ionshi ps bet ween cl im at e,

hum an acti vi t ies and wat er resources. There ar e thr ee di st inct form s of model s, these are

empir ical, conceptual and physi cal ly based distr i buted (Legesse et al . 2002) .

2.1.4.1 Model types

Empirical Models

E mpir ical model s ar e also known as bl ack box models. They do not expli ci t ly consider the

physi cal laws of the processes invol ved, but they rel at e input to output through som e transf or m

f unct ion (Legesse et al . 2002) . These model s ar e convenient to use and easy to cali brate,

however , they need long per iods of meteor ol ogi cal and hydr ol ogi cal dat a that ar e not al ways

avail abl e (Univer si t y of Tokyo, 2003) .

Conceptual Models

Concept ual models repr esent the ef fecti ve response of an ent i re cat chm ent wit hout at t em pt ing

t o expl i ci tl y characteri se the spati al vari abi li t y of t he response. These m odel s are al so known as

grey box models (Legesse et al . 2002) . They are a compr om ise bet ween em pi r ical models and

physi cal ly based model s. They have only a few par am et er s, but unl ike empi ri cal model s, these

par am et ers r etain a physi cal meani ng (Uni ver si ty of T okyo, 2003).

Concept ual models for runof f pr ocesses ar e sti ll pr edom i nant l y li near because of the easi er

m at hemat ical form ul ati on (Wit tenburg, 1999) , alt hough it is unl ikel y that storage ef f ects coul d

be tr ul y linear (Pr asad, 1967). It is the identi f icat ion of the act ual nonl inear it ies that is a step

t owar ds physi call y based modell i ng ( Wit tenburg, 1999) .

Physically Based Distributed Models

T hese models si mulat e pr ocesses using equat i ons der ived fr om the physi cs governi ng the

system and fundam ent al laws of physi cs (Uni ver si t y of Tokyo, 2003). Known as the whi t e box

m odel s, they expl ici tl y represent the spati al var iabi li t y of the land sur face char act er isti cs.

Dependi ng on the scale and accur acies desir ed they repr esent such el em ent s as topogr aphic

elevati on, sl ope, aspect and veget at i on (Legesse et al . 2002) . Alt hough these model s gener all y

i nvol ve hi gh levels of complexi t y wi t h numer ous par am et ers, they ar e sti l l si mpl if ied



r epresentati ons of reali t y (Uni ver si t y of Tokyo, 2003). Sophi st icat ed physi call y dist ri buted

m odel s when appli ed to a large cat chm ent requi re both a gr eat deal of tim e and dat a preparat ion

( Najafi , 2003).

2.1.5 Hydrologic Models

T here have been many dif f er ent att em pts to captur e the response of cat chm ents to rai nfall ,

r angi ng fr om si mple em pi r ical models to highly complex distr i buted model s that incor por at e

t he physical and spati al char act er ist ics of the cat chment as well as int r astorm and int er st orm

var iabi l it y. Ther e are al so models that incorpor ate dif f er ing tim escal es, that is, they model the

r esponse of a storm on an hourl y/dai l y/ mont hly/annual ti mescales to deter mi ne the runof f

gener at i on pr ocesses in the cat chm ent . The level of det ail and compl exit y in any model is

det er mi ned by its purpose and desi red out com e. That is, the model conf igurati on needs a level

of compl exit y that is appropriat e for a part icul ar si gnature of r unoff vari abil i ty ( Jot hi tyangkoon,

2001) . Whi lst the more complex of these models can pr oduce accurate resul ts they requir e a

l ar ge am ount of dat a, whi ch i s oft en ei ther expensi ve or m ay not exi st for many catchment s. To

accur at ely model a system , the requi r ed com plexi t y of the model incr eases wit h decreasi ng

t im escal e and i ncreasi ng catchm ent dr yness (At ki nson et al , 2002) .

P reci pi t at ion and evapor ati ve potent i al are the maj or cont rol s on t he long- term water bal ance at

a regional scal e (F arm er et al , 2003) . The expect ed annual behaviour of a catchment is

charact eri sed by key int eract ions bet ween the cl i mate and the landscape propert i es of a

cat chment (F arm er et al , 2003) . The annual vari abi li ty of a catchm ent refl ects syst em response

t o cl im ati c var iabi l it y (Farm er et al , 2003) .

2.1.6 Antecedent Moisture Conditions

Ant ecedent moisture condi ti ons are im port ant in the process of runof f gener at ion. Far mer et al

conduct ed a study into annual fl ow persistence and pr edomi nant fl ow behaviour wi thin a

t ypical year to identi fy the im por tance of car ry- over ef fect s bet ween months, for example, the

eff ect of pr olonged basef low in the seasonal behavi our of the cat chm ent. It was found that

predi ct i on capabi li t ies for all ti me scal es were st rongl y contr ol led by the ant ecedent condi ti ons

whi ch were r epr esented by t he soil m oisture defi cit ( Far mer et al , 2003) . F iel d capacit y gover ns

t he ant ecedent condi ti ons of cat chments and is the domi nant contr ol on st ream fl ow var iabi li t y

at al l tim escal es (Atkinson et al , 2002) . Fiel d capacit y is a measure of the st or age of the

cat chment and how quickl y i t dr i es out af ter r ai nfall events (Atkinson et al , 2002) .



I n many semi - ar id regi ons the majori t y of runoff is produced fr om extr em ely var i able, high

i nt ensi t y, shor t dur at ion rai nf all events. It is in these sem i- ar id catchment s that the ant ecedent

m oi st ur e condit ions ar e secondar y to the int ensi t y of the st orm in ter ms of cont roll i ng runoff

vol um es. Thi s is consi st ent wit h the fact that moisture ‘m em ory’ is low for sem i -ari d

cat chments as pot ent ial annual evapor at ion exceeds the annual preci pit at i on (Syeda et al ,

2003) .

2.1.7 Effect of Vegetation

S tudi es have shown that the rem oval of deep root ed veget at ion resul t s in an incr ease in the

obser ved runoff from a catchm ent . The accessibil i ty of soi l wat er to root system s has been

shown to be a maj or infl uence upon water bal ance (F ar mer et al , 2003) . A st udy conduct ed in

1989 af t er the cl ear ing of a Col li e River catchm ent found that annual runof f increased fr om 5-

10% of the annual pr ecipi tati on to 30-40% of the annual pr eci pi tati on (Rupr echt and

S choefi eld, 1989) . The evapor at i ve fl ux from a catchm ent is dependent on the per cent age of

veget at i ve cover (F arm er et al , 2003) . Precipit at i on and evaporati on ar e the major contr ol s on

t he l ong-t er m wat er balance of sem i- ari d systems.

2.1.7.1 Bucket Model

T he sim plest model uti li sed to predi ct st reamf low from rai nf all in a cat chm ent is the bucket

m odel , and many models ar e buil t around thi s essent iall y sim ple concept. The bucket model is

t he appl icat i on of the water bal ance equati on to the cat chment, wher e st orage in the catchm ent

i s si mpl y model led as a bucket. S ur f ace runof f is assum ed to gener ate when the water storage

i n the bucket exceeds it s storage capacit y (Jothi tyangkoon, 2001) . The catchm ent is abl e to

absor b all pr ecipit ati on unti l it reaches a cert ain level, then runoff occurs. Thi s is a hi ghl y

sim pl if i ed appr oxim ati on of runoff pr ocesses and does not account for inf il tr at i on excess

r unof f.

Water Balance Equation

T he wat er bal ance equati on is deri ved from the conser vat ion of mass, and can be appl i ed to

dif feri ng spati al and tem poral scales (Robi nson and Sivapalan, 1997) . It st at es that the change

i n st or age of a syst em is equal to the input s mi nus the outputs. The general vol um et r ic wat er

bal ance per uni t sur face ar ea over a shor t per iod of ti m e (m uch less than a year ), for the singl e

bucket model , i s gi ven by


T he Effect o f Clima te Ch a ng e on Strea mflo w in th e S ou th - West Wester n Aus tra lia 1 0

( ) ( ) ( ) ( )tetqtpdt

tdsse −−= Equ at io n 2 -1

T hat is, t he rate of change of storage is equal to the wat er inputs mi nus t he output s. Wher e, p(t )

i s the preci pit at ion i nt ensit y, qs e( t) i s the saturati on excess runof f rat e (t her e is no i nfi lt r at ion

excess runof f i n thi s model ), e( t) i s t he evapor ati on r ate and s( t) is t he volum e of water storage.

T he t wo outf l ows, qs e( t) and e( t) , are descri bed as funct i ons of the storage, s(t ) ( Jothi tyangkoon,

2001) .

2.2 R ainfall in south-west W ester n A ustr alia

2.2.1 Global Climate Change

Cli mate change is a spat i al ly vari abl e phenomenon that is cur rent ly hi ghl y researched. Ther e

have been many st udi es conducted to det er mi ne the dif fer ing eff ects of cl im at e change on

l ocal ised ar eas such as the sout h- west of West er n Austr ali a. Gl obal cl im ate change is dri ven by

t he enhanced gr eenhouse eff ect

T he enhanced gr eenhouse eff ect is the increasi ng absorpt ion of heat radi ati ng fr om the eart h

r esul ti ng in escalat ing global tem per at ur es. The gr eenhouse has concentr ati ons whi ch ar e

r esponsi bl e for the enhanced “t r appi ng” of the heat in the at mospher e ar e increasi ng due to

ant hr opogeni c act ivi ti es (AGO, 2002) .

T he Int ergovernment al Panel on Cli mat e Change (I P CC, 2001) was establi shed to peri odi call y

assess the curr ent sci ence base and to repor t it in a fashion that is readi ly usable by pol i cy

devel opers gl obal ly. It was once debati ng whet her the changes in gl obal cli mate that ar e

cur rent l y occur ri ng were due to gl obal warm i ng of they wer e an outcome of long- t er m cli mate

var iabi l it y. The second IPCC repor t in 1996 pr oved that gl obal warm i ng was occur ri ng and that

t he tem per at ure fluctuat i ons wer e not due to nat ural var iabi l it y as had been pr evi ously

hypot hesised (P earm an and Hennessy, 2002) . The sci ence of cl im at e change is now global ly

accepted, wi t h a pr oject ed warm i ng rate of 1.4˚C to 5.8˚ C bet ween 1990 and 2100 (P ear man

and Hennessy, 2002) .

2.2.2 Climate Change in Australia

T he trends that have been ident i fi ed in Aust rali a are general ly consistent wi th those ident i fi ed

elsewher e in the wor ld ( I PCC, 2001). Mean t emper atures have risen by 0.05-0.01˚ C per decade



over the past centur y, wi th a comm ensur at e increase in the fr equency of ver y war m days and a

decrease in the num ber of ver y col d days (I P CC, 2001) . It has also been not ed that ni ght ti m e

t em perat ur es have ri sen faster than day tim e tem per at ur e, leadi ng to a decr ease in the di ur nal

t em perat ur e range (I PCC, 2001). These changes ar e not unif or m over the cont inent but ar e

highl y regional ised. Nichol ls et al and CSI RO have conduct ed more specif i c st udi es on the

eff ect of cl i mate change on t he sout h-west.

2.2.2.1 Rainfall

Rai nf al l trends are less cl ear due to great er com pl exit y in the processes of cl oud form at ion and

preci pi t at ion (Pear m an and Hennessy, 2002). Rainf al l tr ends in Aust r al ia have hi gh spat ial

var iabi l it y. Incr eases in the fr equency of heavy rainfal ls and aver age rainfall ar e signi fi cant in

m any par ts of Austr ali a, wi th aver age rai nf all havi ng incr eased most in the nor t h- west and

south-east quadrant s (IP CC, 2001). However, extr eme decr eases are pr oj ect ed for spri ng and

winter rai nf all in the sout h- west (CS IRO, 2001). Rainfal l var iati ons are associ ated wit h the El

Niño – Southern Osci ll at ion ( Wr i ght, 1974), al though the r el ati onshi p is weaker than is t he case

f or t he east ern stat es ( McBri de and Nicholl s, 1983) .

2.2.2.2 Evaporation

T he model pr oduced by CS I RO in 2001 based on GCMs (gl obal cl i mate models) projects that

annual evapor at ion wil l increase bet ween 0 and 8% per degr ee of global warm ing. St udi es have

been conduct ed which show a general decrease in pan evapor at i on in many par ts of Aust rali a

f or the last 30 year s. This was unexpected due to the temper ature incr ease associated wit h

global war mi ng. Pan evaporati on has now been linked mor e closel y to cl oud cover and wind

eff ects (Roderi ck, sem inar, 2003). This conclusi on has not been ext ended to the sout h-west of

WA. Since WA does not behave as the rest of Aust r al ia, mor e ext ensi ve research would be

needed bef or e such a conclusi on coul d be dr awn.

2.2.3 The Climate of south-west Western Australia

T he sout h- west of West er n Austr ali a is a sem i- ar i d envi r onment wher e annual pot ent ial

evaporat ion exceeds the annual preci pit at ion. It has a Medit err anean cli m at e wi t h hot , dr y

sum mers and mil d, wet wi nters. It is a hi ghl y seasonal cli mat e wi th 80% of the tot al annual

preci pi t at ion f al li ng dur ing the wint er m onths of May t o Oct ober (Ni chol l s et al . , 1999). 60% of

t hi s rai nf al l can be att r ibut ed to the successive passage of cold fr onts that cr oss the sout h- west

cor ner of the state duri ng wi nt er (Hughes and Gut torp, 1999) . A decr ease in r ai nfall is observed



dur ing the summ er when gl obal ci rcul ati on patt er ns force these syst ems sout hwar ds. Possible

i nf luences on winter r ai nfall to account for t he ot her 40% ar e mechani sm s such as the Leeuwi n

cur rent , nor t h- west cl oud bands, the El Niño Sout hern Osci ll ati on, and the Antar ct ic

Cir cumpolar Wave (Ni chol l s et al , 1999) .

F ront s are narr ow zones of changing condi ti ons wher e one air mass meet s another ai r mass of

dif ferent tem peratur e and hum idi ty. If the col d air is act ively pushing beneath the war mer air

t hen it is known as a col d fr ont . They resul t in cl oud cover and pr eci pi t at ion accor ding to the

condi ti ons i n t he ai r masses invol ved ( Br adshaw and Weaver , 1993) .

T he sout h- west of West er n Austr ali a exper iences a higher annual rai nfall than si mi lar lat it udes

around the gl obe. This phenom enon is pr im ar i ly due to the Leeuwin Curr ent . The Leeuwi n

Cur rent or igi nates in the Pacif i c Ocean and fl ows down the west coast of West er n Aust rali a,

i ncreasi ng the sea sur face temperatur es. St r ong westerl y winds advect thi s moist air inland and

hence i ncr easing the probabil it y of rai n (Bates et al , 1998) . Due t o the inf l uence of the coast in

t he producti on of rainfal l in the south-west , there is a signif icant inl and rai nfall gr adient. In the

Col li e River Basi n annual average rai nf al l decreases fr om 1100m m to 550m m f rom the coast to

t he inl and, approxi m at el y 90km inl and. Pan evapor at ion exper i ences a cor r espondi ng decr ease

of 1600m m to 1400mm (Jot hit yangkoon et al , 2001) .

2.2.4 Changes to rainfall in the south-west rainfall

Whi lst the temper at ure ri se in the sout h- west is at tr ibuted to the enhanced greenhouse ef fect,

t he decr ease in rai nfall is a combined resul t of natural var i abil it y and the enhanced greenhouse

eff ect (AGO, 2002). The south-west of Wester n Austr al ia has seen a 10- 20% decrease i n winter

r ai nf al l over the last 30yr s. This was not a gradual decli ne, but a rapi d change of rai nf al l

r egim es in the mi d 1970s (AGO, 2002) . Thi s decli ne has manif est ed it self as a st ep change to

an al ter nati ve, dri er st ate whi ch has fewer rain days and less rain in extr em e days than for

previ ous decades (I OCI , 2002) . The peri od fr om May to October is the most impor t ant for

r ai nf al l in south West er n Austr ali a (Ni chol l s et al . , 1999) . The rai nf all decr ease was obser ved

onl y in earl y winter rai nfall (Bat e, 2002). Changes in the number of rai n days, and the amount

of rain fall i ng in ext rem e event s, have cont ri but ed to the tr ends in tot al rainf al l (Ni chol l s et al . ,

1999) .



F ig ure 2 -1 : T rend s in w in ter rainfa ll in M an jim up , Western Au stra lia (sou rce B ate, 20 02 ).

F igur e 2.1 di splays the winter rai nf all for Manj i mup fr om 1910 to 1997. The fluctuat i ng red

l ine shows the ti me seri es of wi nt er rainfal l, the fl at t er red li ne is the sm oot hed dat a. The dat a

was smoothed by appl yi ng the di stance wei ght ed least squar e met hod (Bate, 2002) . The two

hor izont al , green li nes are mean annual rai nfall s averaged for the per iod bef or e 1970 and the

per iod aft er 1970. As shown, Manji mup has experi enced a decr ease in rainf all of appr oxi matel y

25% , fr om 800mm to 600mm . Thi s decrease in mean has been accompanied by a decrease in

t he standard devi at i on and extr eme rainfall event s, as can be seen by the reduced var iabi li t y in

winter rai nf all aft er 1970.

2.2.4.1 Changes to mean annual rainfall

Changes to annual rainfal l ar e not unif or m thr oughout the south-west of Western Aust r al ia.

T hat is, the changes cont ai n spati al vari abi li ty. Figur e 2.2 shows the changes in annual rai nf al l

f or four stat ions in the sout h- west, namely Pembert on, Per th, Manji m up and Al bany. The data

was separated int o two peri ods, one bef or e and one af ter 1975 and the annual mean over each

of the tim e per iods was cal culat ed. The two means are repr esent ed by the bl ue and red col um ns

r espect i vely. The rati o bet ween the two annual means was cal cul at ed to gain an under standing

of the change i n mean annual rai nf al l t hat occur r ed i n the m i d 1970s at each locat ion.

P em bert on, Pert h and Manj im up al l show si gni fi cant decl i nes in mean annual rainf al l wit h

r at ios of 0. 91, 0.90 and 0. 88 corr espondi ng to decr eases of up to 10%. Al bany, however, has a



r at io of 0.98, indi cat ing that rai nf all on the sout h coast of Wester n Austr al ia has decli ned by

j ust 2% si nce the mi d 1970s. The var i ance between the st at ions in the am ounts of change of

t heir mean annual r ainfal l values indicat es that the changes that occurr ed in t he mi d 1970s were

not spat iall y uni for m over the south- west of West er n Austr al i a.

Fig ure 2 -2 : The rela tiv e ch an g es t o mea n a nn ua l rainf a ll in t he mid 19 7 0s a t f ou r v ario us lo ca t io ns in t hes ou th -w est o f Wes tern Au s tralia , Pemb erto n, Pert h , Ma nj imu p a nd A lb a ny .

T he mean wint er rai nfall has decreased by 10% wi t h a cor responding decrease in the st andard

deviati on of 31% si nce 1975. A decrease in the st andard devi ati on of the rainfal l wi l l have

signi fi cant impli cat ions for sur face runoff regi m es. The standard devi at i on can be used as a

m easure of aver age int ensit y of the rai nf al l , thi s has reper cussi ons on the amount of wat er that

i nf il tr ates int o the soi l .

2.2.5 Effect of climate change on south-west rainfall

P redi ct i ng the ef fects of global war m ing on futur e rainf al l is di ff i cult . Most model s predi ct a

l ar ge decr ease in the mean wi nt er rai nf al l for the sout h-west , combi ned wit h a rise in aver age

t em perat ur e (AGO, 2002). CS IRO model l ing based on the IP CC Special Repor t on Em i ssions

S cenari os (S RES ) shows ranges of proj ected changes to Aust ral ian rai nf al l for the years 2030

and 2070 (CS I RO, 2001) . Figur e 2.3 shows the proj ected ranges for change in rai nfall for

Austr al i a for 2030 and 2070 rel ati ve to 1990 (CS I RO, 2001) . Annual rai nf all in the sout h- west



i s pr edi ct ed to range fr om +5% to -20% by 2030 and +10% down to as lit tl e as 60% by 2070.

T hi s decli ne is not unif orm thr oughout the year, wi th most of the decl ine occur r ing in the

winter and spri ng m ont hs, wit h lit tl e change i n the sum m er r ainfall (CSI RO, 2001).

De Souza conducted a study in 2002 which rel at ed large scale at mospher ic pr ocesses to the

probabi l it y of wi nt er rai n in t he south-west of Western Aust r al ia. The m odel included m ean sea

l evel pr essur e, the nort h-south gr adi ent of mean sea level pr essure and dew poi nt tem peratur e

depressi on.

A spati al rai nf al l index was const ructed whi ch relates the pr obabil i ty of south- west wi nt er

r ai nf al l back to lar ge scal e at m ospheri c var iabl es to quanti f y the decli ne of wi nt er rainfal l that

occur red in the sout h- west in the mi d 1970s. Thi s was achi eved by the obser vati on of actual

dai ly pr ecipi tati on at 10 stati ons si tuat ed throughout the sout h- west whi ch wer e classi fi ed as

one of six weat her states. By noti ng how the atm ospheri c measur e would change in the future

usi ng curr ent trends and gl obal cl im ate modell ing scenar ios, the SRI coul d be used to inf er

weather st at es and their associ ated preci pi t at ion occur r ence.

A stati sti cal popul ati on is the set of al l measur em ents corr espondi ng to each paramet er in the

ent ir e popul ati on of uni t s about whi ch infor mati on is sought (Johnson, Bhat tacharyya, 1996) .

T he populati on param et er s of thi s st udy wer e par t of the lar ger geophysi cal envi ronm ent .

Cli mate change is pr oj ect ed to lead to an incr ease in the occur rence of the populati on

par am et ers.

F ut ur e winter rai nf all in the sout h- west was predicted usi ng GCM dat a and ext rapol at ed

t rendli nes based on the histori cal vari at ion in the populati on param et er indi ces and thei r

cor relat ions to the SRI. The resul ts of thi s modell ing indicated that the probabil it y of wi nter

r ai nf al l in the sout h- west wi ll decr ease to appr oxi matel y 58 percent in 2065. Modell i ng of

predi ct ed CS I RO GCM Mk 2 data of the at mospher ic pr edict or s MSL P, N- S MS L P gr adi ent

and the dew poi nt temper ature depr ession in 2065 indi cat es that rai nfall in the sout hwest coul d

decrease by 45 per cent (De S ouza, 2002 unpub) .



F igur e 2-3: Ranges of average seasonal and annual r ai nfall change (% ) for around 2030 and2070 rel at ive t o 1990. T he coloured bar s show ranges of change for areas wi th corr espondi ng

col ours in t he maps. Ranges are not given f or ar eas wit h seasonal ly low rai nf al l becauseper cent age changes in rai nf al l cannot be as reli abl y cal culat ed or appli ed in such r egi ons

C ha pter 3: Site D es c ription

T he Effect o f Clima te Ch a ng e on Strea mflo w in th e S ou th - West Wester n Aus tr alia 1 7

3 Sit e De s cr ipt ionT he sout h- west of West er n Austr ali a cover s a lar ge ar ea in which there ar e st rong spati al

cli mati c gradient s bot h in the lat it udi nal and longit udi nal dir ecti ons. As an exam pl e of par t of

t he area, the Col li e River Basi n ext ends approxi m at el y 100km inland, t owards the east , and the

annual average rainf al l decreases fr om 1100m m to 550m m. This decr ease is accompani ed by

cor responding decreases in potenti al evapor ati on and runof f (Jothit yangkoon et al. 2001). This

eastwar d decr ease in longit udinal rai nf al l gradi ent is lar gel y due to the decrease in coast al

i nf luence. Ther e is al so a sout hward decr ease in rainfal l gr adi ent in the south- west . Average

annual rai nf all i ncr eases signi f icant ly f rom P er t h to August a.

As di scussed in Chapter 2, the changes in cl im at e t hat have aff ected t he sout h- west over the l ast

30 year s have not been spat iall y uni f or m. Fr om this it is reasonabl e to assum e that fur ther

cli mate changes to the regi on wi ll be spati all y var iabl e. However , there ar e no pr oj ect ions for

cli mate change on a more local scale, so for the purposes of this st udy changes have been

assum ed to be spati all y uni form for all of the sout h- west.

T he r unoff pr ocesses, that ar e dom inant aft er each rainfal l event , are determ ined not onl y by the

charact eri st i cs of the rainfall but are by a highly com plex int er act ion of many catchment

charact eri st i cs. The land use of a catchm ent is of gr eat inf l uence on the dom inant runoff

gener at i on m echanism . This is because t he l and use si gni fi cantl y af f ects the inf il tr ati on capaci t y

of the soi ls. Urban or cl eared cat chm ents have far lower inf i lt rati on capacit ies and ther ef ore

r unof f is mor e li kel y to be produced by inf i lt rat ion excess overl and flow. Vari ati on in

cat chment topography can be expect ed to play a si gnif icant role in the pr ocesses of runof f

gener at i on. The dom i nant mechani sm for runof f pr oduct ion is dependent on hi ll sl ope.

S at ur at i on excess r unoff is m or e dom i nant i n r egi ons wi t h st eep slopes.

T he cat chm ent s used in this study ar e those in the sout h-west cor ner of Western Aust r al ia,

F igur e 3.1. The sout h- west dr ai nage division consists of 17 basins, and most ar e nam ed af ter

t he maj or ri ver s. The basins wer e uni quel y nam ed and num bered by the Aust rali an Water

Resources Council (AWRC) in the 1960s (WRC, 2003) . These nam es and num ber s ar e shown

i n tabl e 3.1. T he r ainfal l data was provi ded by the Bur eau of Met eor ol ogy.

S tr eamf l ow i s m easur ed by gauges cont roll ed by t he Water and Ri vers Comm i ssion.



Fig ure 3 -1 : Lef t: Th e maj or d raina g e ba sin s of West ern A us t ra lia, Righ t : Th e maj or draina g e ba s in s of th es ou th -w est d raina ge divis io n (WR C, 2 0 03 )

Tab le 3 -1 : The A WRC n u mb ers a nd n a mes of th e d ra in ag e b as ins in t he s o ut h- wes t of West ern A us t ra lia( WR C, 2 0 03 )

Drainage Basi n

– num ber

Drainage Basi n -

nam e

Drainage Basi n –

num ber

Drainage Basi n - nam e

601 E sper ance Coast 610* Busselt on Coast

602 Albany Coast 611 P rest on Ri ver

603* Denmark Coast 612* Col li e River

604 Kent Ri ver 613 Har vey River

605* F rankland Ri ver 614 Mur ray River

606* S hannon Ri ver 615 Avon Ri ver

607* War ren River 616 S wan Coast al

608 Donnell y River 617 Moore-Hi ll Ri vers

609 Blackwood Ri ver

An aster ix ( *) marks t he catchm ent s, or basi ns, that wer e ut i li sed for t his study.

I n al l of the f ol lowing figur es showi ng cat chm ent m aps, st reamflow gauges are depi ct ed in bl ue

whi lst the rainfall gauges ar e depict ed in red. The land use maps for the River basi ns selected

f or t hi s study ar e included i n Appendix B



3.1 612 – C ollie Basin C atchm ent

Fig ure 3 -2 : A map of t h e Co llie Riv er Bas in

3.1.1 Site Characteristics of the Collie River Basin

T he boundari es of the Col li e Ri ver Basi n ar e shown in Fi gure 3. 2. It is a lar ge semi - ar id

cat chment cover ing an approxi mat e ar ea of 3 580 km2 .

3.1.1.1 Drainage Network

T he Col l ie Ri ver is the dom inant river syst em ext endi ng near l y 100 km inl and. A number of

m aj or tr ibut ari es br anch ar e the Wel l esley, Br unswi ck, Har ri s and Bi ngham river s. Their

headwat ers st ar t on the Dar li ng Range and the edge of the Yi l garn Pl at eau. Downstr eam of the

Wel li ngt on Dam, the Coll i e Ri ver traver ses the forest ed Darl i ng Scar p bef or e it cr osses the

S wan Coast al Pl ai n and di scharges int o Leschenaul t Inlet . Lar ge reaches of the Col li e River

have been tr ained to prevent fl ooding of townshi ps (i ncl uding Col li e) and coal mining areas

( WRC, 2003).

3.1.1.2 Rainfall

T here is a si gnif icant decr easi ng rai nf al l gradi ent from west to east: aver age annual rai nf all

decreases fr om 1100m m on the coast to 550mm on the east ern boar der. The long ter m ar eal ly

averaged rai nfall is 720m m wi th an aver aged pan evaporat ion of 1500m m (Jothit yangkoon et

al. , 2001) .



3.1.1.3 Topography, landforms and soils

Val ley side slopes in the wester n par ts of the catchm ent are about 10- 20% . In the eastern part s

of the cat chm ent these decr ease and are less than 10% repr esent ing a landscape of br oad, fl at

val leys (Jot hit yangkoon et al , 2001) .

T he Col l ie Ri ver Basin has dupl ex soi ls consisti ng of pr edom i nant ly gr avely and sandy l at er i ti c

sur face soil s wit h high hydraul i c conduct ivi ti es. These soil s overl y a thick, deep kaol init i c cl ay

l ayer of l ow perm eabil it y ( Jothi tyangkoon et al , 2001)

3.1.1.4 Land use

T he Col l ie Ri ver Basin incorpor ates var ied land uses, incl udi ng agr i cult ure, mi ning and

f or estr y. Int er pr et ati on of Landsat TM im ages (vi si bl e bands) indicates that 30% of the

cat chment has been clear ed of nati ve vegetat ion and r epl aced by annual past ur e.

S al inisati on of sur f ace wat er s has become the lar gest water quali ty issue in the Col l ie River

Basin wi th many rivers adversel y impact ed by land clear i ng and a ri sing sal ine wat er tabl e.

T hi s is most evident f or ri vers and str eams in t he east ern-m ost par t of the basi n. E l sewher e high

l evel s of cl ear ing and inappr opr iate agri cul tural pract i ces, ni tr if i cati on, and si lt i ng up of the

r iver s and estuar y have becom e impor t ant issues. Local wat er qual it y issues may al so be

associat ed wi th modi fi ed ri ver flows and dewat er i ng act i vi ti es fr om mi ni ng acti vit ies in the

Col li e coalf i el d.

T he land uses presented in the tables of dat a descr ipti ons f or the str eam fl ow st at ion cat chm ents

are r epr esent at ive of the dom inant l and use wi thi n the cat chm ent of the gaugi ng st at i on

3.1.2 Streamflow sites

Tab le 3 -2 : Descrip tio n of t h e st rea mf lo w s ta t io ns w ith in th e Co llie R iv er Ba sin

S tati on 612002 612152

P er iod of Recor d 1970- 2002 1962- 1982

Cat chment Ar ea [km2 ] 2538 210

River Col li e River Brunswi ck Ri ver

L and Use F or estr y Dryland Agri cul ture/ Li vestock Gr azing



3.1.3 Rainfall sites

Tab le 3 -3 : D es cript io n o f th e rain fa ll s t at io n s with in th e C ollie R iv er Ba sin

S tati on P er iod of Recor d L and Use Distance from coast

[ km ]

9657 1950- 2002 Dryland Agri cul ture 9

9601 1988- 2002 Dryland Agri cul ture 19

9513 1950- 2002 Dryland

Agr icul t ur e/ L ivestock

Grazi ng

15

9628 1950- 2002 F or estr y 43

9738 1963- 2002 Mining 58

9914 1973- 2002 L ivestock gr azi ng 90

3.2 603 – D enm ar k C oast Basin

Fig ure 3 -3 : A map of t h e Denma rk C o as t Ba s in



3.2.1 Site Characteristics of the Denmark Coast Basin

T he boundari es of t he Denmark Coast Basin ar e shown i n Figur e 3.3


T he Denm ar k and Hay ri ver s domi nat e sur face dr ai nage in the basin and begin about 80 km

i nl and. Both ar e per manent ri ver s. Wi lson I nlet recei ves f lows fr om both the Denmark and Hay

r iver s and is breached to the ocean on an annual basi s usual l y foll owi ng earl y winter rai nf all .

Mar bell up Br ook and Torbay Main Dr ai n are t wo examples of sm all r iver systems i n t he sout h-

east of basi n t hat drain a coast al , high rai nf al l r egion and di schar ge i nto T or bay I nlet.

3.2.1.2 Rainfall

T hi s is a hi gh rainf al l area, aver agi ng over 1200 mm on the south coast. Inland, the aver age

annual rai nf all decr eases to ar ound 700-800 mm . About two- thi rds of the annual rai n fal ls in

t he six mont hs fr om May to October . Seasonal rai nfall and pot enti al evaporati on di ct ate the

l engt h of the growi ng season, which var ies bet ween 7 and 10 months in the sub-r egi on.

3.2.1.3 Geology, Topography, soils and landforms

Most of the area is under lain by the Al bany- Fr aser Or ogen, which is composed of gnei ssi c and

grani ti c rocks of Pr ot er ozoic age. Many of the soil s have poor dr ai nage, wi th rain-borne sal t

accum ul ati ng in deep soi l profi l es. Rising groundwater level s caused by the cleari ng of nat i ve

veget at i on has mobi l ised these sal ts.

Many of the soi ls have been for m ed on highl y weat hered par ts of the later it e pr ofi le, and ar e

i nf er ti l e. Tr ace el ement s have to be added to lat er it ic soil s, and nit rogen has been appl ied bot h

as a fer ti li ser and by gr owing of legum es. Leachi ng of the soil s in the high rai nf al l areas has

r esul ted i n their becomi ng acidi c.

3.2.1.4 Land use

Most of the Denmark Ri ver cat chm ent rem ai ns forested and suppor ts potabl e wat er suppl y

dam s - the largest of whi ch has recentl y cl osed due to rising sal t level s. Wi despr ead clear i ng of

l and for agr i cult ur e in the upper Hay River catchment and eastern part s of the basin has

contr ibuted to incr eased nutr ient and sedim ent l oads to the inl et .




Tab le 3 -4 : Descrip tio n of t h e st rea mf lo w s ta t io ns w ith in th e Den ma rk Co as t Bas in

S tati ons 603136 603190

P er iod of Recor d 1961- 2001 1964- 2001


River Denmark Ri ver Yat e Fl at Cr eek (Denmark

R.)

L and Use Dryland Agri cul ture Nat ur e Conser vati on


Tab le 3 -5 : D es cript io n o f th e rain fa ll s t at io n s with in th e D en ma rk Co a st Bas in

S tati on 9558 9531 9595

P er iod of Recor d 1950- 2002 1950- 2002 1950- 2002

L and Use Nat ur e

Conservati on

Dryland

Agr icul t ur e

L ivestock

grazi ng

3.3 605 – Fr ankland R iver Basin

Fig ure 3 -4 : A map of t h e Fran k la nd River Bas in



3.3.1 Site Characteristics of the Frankland River Basin

T he boundari es of t he Fr ankland Ri ver Basin ar e shown i n F igure 3.4.


T he F rankl and River is t he domi nant river syst em in a basi n that cover s 4 550 km 2 .

3.3.1.2 Rainfall

T he annual rainfall in the catchment ranges fr om 1200m on the coast to just 600m m on the

nor ther n bor der in the Yi lgar n Plateau. In thi s nor ther n area the headwat er are known as the

Gor don River , whi ch has ill -def i ned drainage lines that join and fl ow int o salt lakes dur ing

excepti onall y high rai nf all year s. But, most of the flow ori ginat es fr om the lower half of the

F rankland Ri ver whi ch di schar ges i nt o Wal pol e Inl et .

3.3.1.3 Land use

Most of the river syst em s are br acki sh to sali ne which can be att ri but ed to hint er land salt lakes,

sal ine groundwater and incr eased cleari ng in the upper cat chm ents. Clear i ng in upper

cat chments has al so cont r ibut ed to er osion probl ems and incr eased sedi mentati on of many r iver

channel s and pool s. Dense f or est s rem ai n in the lower part of t he catchm ent area.


Tab le 3 -6 : Descrip tio n of t h e st rea mf lo w s ta t io n wit hin t he Fra n klan d R iv er Ba sin

S tati ons 605012

P er iod of Recor d 1953- 2001


River F rankland Ri ver

L and Use Nat ur e Conser vati on


Tab le 3 -7 : D es cript io n o f th e rain fa ll s t at io n w it hin t he Fran kla nd R iver Ba s in

S tati on 9661


L and Use Dryland Agri cul ture



3.4 606 – S hannon R iver Basin

Fig ure 3 -5 : A map of t h e Sh an n on R iver Ba s in

3.4.1 Site Characteristics of the Shannon River Basin

T he boundari es of t he Shannon Ri ver Basin ar e shown i n figur e 3.5.


T he Shannon River Basi n cover s an ar ea of 3 200 km2 . The three lar ge ri ver systems that

dom inat e the surf ace drai nage ar e the Shannon, Gardner and Deep Rivers. All begi n about 50

km inland on the souther n Dar li ng Pl ateau and the S cott Coast al Plai n. Large wet lands, such as

t he Mui r wet l and system, ar e com mon in the upper basi n, but are onl y hydr ol ogicall y

connect ed to ri vers in t i mes of fl ood.

3.4.1.2 Rainfall

Annual average rainf al l is the highest for the sout h- west region, general ly bei ng in excess of

1,200-1, 400 mm.

3.4.1.3 Land use

Most of the Shannon Ri ver Basin remai ns predom inant ly forest ed in a nati onal par k, wi th onl y

par ts of t he Meer up, Gar dner and Wal pol e Ri ver catchm ents bei ng cleared for agr i cult ure. Most

of the sur face water ways remain in near pri sti ne condit i on.




Tab le 3 -8 : Descrip tio n of t h e st rea mf lo w s ta t io ns w ith in th e Sh a nn on River Bas in

S tati ons 606185 606195

P er iod of Recor d 1965- 1998 1965- 2001


River S hannon Ri ver Wel d Ri ver

L and Use Nat ur e Conser vati on F or estr y


Tab le 3 -9 : D es cript io n o f th e rain fa ll s t at io n w it hin t he Sh an no n R iv er Ba sin

S tati on 9611


L and Use Nat ur e Conser vati on

3.5 607 – W arr en River B asin

Fig ure 3 -6 : A map of t h e Wa rren Riv er Bas in

3.5.1 Site Characteristics of the Warren River Basin

T he boundari es of t he War ren Ri ver Basi n ar e shown in F i gure 3. 6.




T he War r en Ri ver Basin covers an area of 4 320 km 2 . The main headwater s of the War ren

River ar e the Tone River that dr ai ns fr om the Yi l garn Pl at eau. Tall karr i for est s ar e a

charact eri st i c feat ure of the basi n to the south and west of Manj im up. Near the coast the ri ver

f lows through densel y the densel y veget at ed, swam py Scot t Coast al Pl ai n and acr oss coastal

dunes before di schar gi ng di rect l y to the ocean. Low seasonal fl ows in sum mer resul t in a sand

bar at the r i ver mouth.

3.5.1.2 Rainfall

Annual rai nf all ranges fr om about 600mm on the Yi lgar n Plateau to 1000mm to 1400mm

f ur ther downstr eam on the War ren River.

3.5.1.3 Topography soils and landforms

L and in this regi on is undulati ng to fl at and is largel y cleared wi t h indiscerni bl e drainage lines

bet ween inland lakes ( Uni cup wet lands) and river syst em s.

3.5.1.4 Land use

L and cl ear ing in the upper Tone Ri ver cat chm ent has exacer bat ed river sal init y, whil e the

r el at ively pr isti ne forested condi ti on of the lower War r en Ri ver and Lef r oy Brook cat chment s

have resul ted in wat er fl ows rem ai ni ng pr edomi nantl y fr esh. Nut ri ent and sedi ment level s ar e

l ikel y to be el evat ed for upstr eam ri ver reaches wher e the land has been wi dely cl ear ed for

agr icul t ur e, mainly catt l e farm i ng.


Tab le 3 -1 0: Descrip tio n of t h e st rea mf lo w s ta t io n wit hin t he Warren R iver Ba s in

S tati ons 607144


Cat chment Ar ea [km2 ] 461

River Wil garup River

L and Use F or estr y




Tab le 3 -1 1: D es cript io n o f th e rain fa ll s t at io n w it hin t he Wa rren River Bas in

S tati on 9530

P er iod of

Recor d

1950- 2002

L and Use F or estr y



3.6 610 – B usselton C oast Basin

Fig ure 3 -7 : A map of t h e Bu ss elt on Co as t Bas in

3.6.1 Site Characteristics of the Busselton Coast Basin

T he boundari es of t he Busselt on Coast Basin ar e shown i n F igure 3.7.


T here ar e 26 shor t river i ne syst em s that di schar ge ei ther di r ectl y to the ocean or indi rect l y

t hr ough coast al lagoons. The basin covers an area of 2 970 km 2 . The nort her n coast al ar ea

r ecei ves inf l ows fr om ni ne shor t rivers and st reams that drai n fr om the edge of the Dar li ng and

Wicher Scarp and the Swan Coast al Pl ain. The Capel, Ludl ow, Abba and Sabi na rivers ar e

l ar ger river syst em s that drain the predomi nantl y for est ed Wi cher Scar p. Lower reaches of the

r iver s and cr eeks have been modi fi ed using art if i ci al dr ai ns to assi st rapi d dr ainage of fl at, low-

l yi ng, clear ed coast al pl ai n.

S ur face water quali t y for wat er ways in the Bussel ton Coast Basi n is extr emely vari ed. Lar ge

areas cl eared for agri cul ture or hor t icul tur e on the eastern and nor ther n coast al regions have

sever el y impact ed surf ace wat er s wit h high level s of nut ri ent s and sedim ent s. This poses

eut rophi cati on (per sistent bl ue- gr een algal bl oom s) and sedi m entati on rel at ed pr oblem s in the

Vasse-Wonner up estuari es. Sur face wat er qual it y for wat erways draini ng the sout her n and



western half of the catchment is com par at ively good due to large ar eas of rem nant nat ive

veget at i on.

3.6.1.2 Rainfall

T he Bussel ton Coast Basi n exper i ences high rai nf all l evels of about 800 - 1200 mm per annum .

3.6.1.3 Land use

L and use in thi s region consi st s lar gel y of dair y far mi ng, vi ti cult ure, other agri cul tural

act ivit i es, and str i p mi ning for heavy mi ner al sands. Foll owi ng mini ng the land is reinst at ed in

accor dance wi th the st ri ct envi r onmental gui deli nes to provi de for nat ur al veget at ion of

agr icul t ur al land use. F i ve of the ni ne rivers on the nort her n edge of the basi n have been leveed

and divert ed fr om the Vasse-Wonner up estuar i es and coast al comm unit i es to discharge dir ectl y

t o the ocean. Sevent een small cr eeks di schar ge to the west er n side of the basin and drain

r em nant coast al scr ub. Margar et Ri ver is the lar gest of these and dr ai ns the nor th-west er n

cor ner of the Blackwood Plateau wher e lar ge forests, li vestock gr azi ng and vi neyar ds ar e

com mon.


Tab le 3 -1 2: Descrip tio n of t h e st rea mf lo w s ta t io n wit hin t he Bus s elto n C oa st Ba sin

S tati on 610001


Cat chment Ar ea [km2 ] 438

River Mar garet River



Tab le 3 -1 3: D es cript io n o f th e rain fa ll s t at io n w it hin t he Bu ss elt on C o as t Ba s in

S tati on 9574



C ha pter 4: Mo de l De v elop men t an d App lic atio n


4 M odel D e ve lopme nt a nd Applica tion4.1 Model D evelopment

T he stat ions select ed for t hi s study were select ed on t he basis that t hey had l ong peri ods

of recor ds and good qual i ty dat a. The locat i ons of the gauges used in thi s sect i on ar e

shown on the basi n maps in Chapt er 3. Rai nf all st at ions were select ed ini ti al ly because

t hey wer e in cl ose proxi m it y to the rel evant str eam fl ow gauge. It was ini ti al ly assum ed

t hat, due to the hi gh spati al vari abi li ty of rai nfall , any rainfall st at i ons that wer e di st ant

f rom the rel evant st ream f low gauge woul d pr ovi de poor corr el ati ons bet ween rainf al l

and runoff in t he catchm ent . However , t hi s lat er pr oved not to be the case. T he di st ance

bet ween the rai nf al l stat ion and the st ream f low gauge was not impor t ant provi ded that

t he rai nfall gauge was repr esent at ive of the rai n fal li ng wi t hi n the cat chm ent of the

str eamf l ow gauge.

T wo dat a set s wer e sel ect ed from the Coll ie Ri ver Basin, nam ely str eam fl ow si tes

612002 and 612152 were anal ysed in detail duri ng the model developm ent pr ocess. The

r esul ts were then extended to seven other catchm ent s to test the sensi ti vit y of the

devel oped model . These catchm ent s wer e 603136, 603190, 605012, 606185, 606195,

607144 and 610001. For the devel opment of the model , st ati ons used wer e those that

had per i ods of recor ds that spanned the cli m at ic change of the mi d 1970s. Thi s was to

ensur e that the m odel was not sensit i ve t o the cl im at ic regi m e of t he region.

T here were two st ages to this st udy. The fi r st was to pr oduce a model that accur at el y

predi ct s str eam fl ow for a cat chm ent given only si mple rainfal l input s. The second st age

was to apply future cl im ate scenar ios to the developed model to predict how cli m at e

change may ef fect st ream f low in the south-west of Wester n Austr al ia.

I n the model it was assum ed that annual str eam fl ow was a functi on of annual rai nfall ,

( )RfQ = , where Q is the annual st reamf low and R is the annual rainf al l. This model

all ows for a ti me lag bet ween the rai nf al l event and the occurr ence of st ream fl ow. The

success of the model in each cat chment was measur ed by the corr el at i on coef fi ci ent , r,

bet ween the predi ct ed (or sim ul ated) st ream f low and t he actual st reamf low.



T he mat l ab codes for all pr ogram mi ng complet ed in thi s study is provided in appendix

A

4.1.1 Memory

A model that incorporated the concept of annual str eamf l ow memory was investi gat ed.

T he t im e ser i es of annual r ai nf all and annual st r eamf low are di splayed i n F igur e 4.1.

Fig ure 4 -1 : Time s eries f or a n nu al ra in fa ll an d a nn ua l s treamflow ov er th eir p erio d s of reco rd s .

Aut ocor r el at i on is the corr el at i on of a var i able wi th it self over successive ti m e int er vals

( Johnson, Bhatt achar yya, 1996). It can be used to det er m ine the ext ent to whi ch the

str eamf l ow i n a par t icul ar year is dependent on the str eam fl ow in t he pr evi ous year, (t he

cat chment has annual str eam fl ow memor y) . In or der t o test annual st r eamf l ow m em ory,

t he aut ocorr elati ons wer e det er m ined for var ious catchm ent s wit h a tim e lag of one

year. The resul ts ar e shown in table 4. 1, fr om which it can be concl uded that the

aut ocor r el at i ons ar e suf f icient l y low to al l ow the concept of an aut or egr essi ve model to

be di scarded.

Tab le 4 -1 : The a u to reg ress io n s of va riou s s ta t io ns in t he so ut h- w es t

S tati on 612002 603190 605012 606185 607144 610001

A utocor r elation

( time lag = 1 year )

0.05 - 0. 06 - 0. 03 - 0. 06 0.04 0.14



4.1.2 Linear Model

T he sim plest model to pr edi ct st ream f low fr om rai nf al l is the linear model.

baRQ += where Q is the annual st reamf low, R is the annual rainf al l and a and b ar e

cat chment dependent param et er s. Fi gur e 4. 2 shows the raw dat a and the si m ul at ed data

obt ai ned f rom t he l i near model.

Fig ure 4 -2 : R aw s t reamf lo w da t a an d t he s imu la t ed s treamflo w da ta us in g t he linear mo del.

E xami nat ion of Fi gur e 4. 2 reveal s that a si m pl e linear model is not the “best f i t” for the

r aw str eam fl ow data due to the broad spread of points across the li near model . For

per iods of lower rai nf al l , the model underpr edict s the act ual str eam fl ow. As the annual

r ai nf al l incr eases to mor e than 800m m thi s trend reverses and the model begins to

overpredict the annual st ream fl ow. However, for high rai nf al l years or 1 100m m or

m or e the m odel once agai n under predi cts t he st reamf low.

T he cor r el at i on coef fi ci ent s di spl ayed in tabl e 4.2 are vari abl e, these resul ts ar e

dependent on how appropr i at e the rai nfall st at ion has been in representi ng the act ual

preci pi t at ion t hat has occurr ed wi thi n the cat chm ent of the str eamf l ow gauge.



Tab le 4 -2 : C orrelat io n coeff icien t s to meas ure t he s u cces s o f th e lin ear mod el.

S tr eamf l ow

gauge

P er iod of

r ecor d

Cat chment ar ea Rai n Gauge Cor relat ion

coeff ici ent

612002 1970- 2002 2 538 9628 0.86

612152 1962- 1982 210 9513 0.89

603136 1961- 2001 532 9531 0.63

603190 1964- 2001 57 9595 0.66

605012 1954- 2001 4520 9661 0.77

606185 1965- 1998 407 9611 0.88

606195 1965- 1998 250 9611 0.85

607144 1962- 2001 461 9530 0.82

610001 1971- 2002 438 9574 0.86

4.1.3 Use of Multiple rainfall stations

S tati ons 603136 and 603190 have poor corr el ati on coef fi cient s (less than 0. 7) between

t heir raw annual rai nf al l and annual st ream f low dat a. This indi cates that eit her an

i nappropri at e rai n gauge has been sel ected, that does not accur at el y ref l ect the rai nfall

occur ri ng in the cat chment, or that the processes invol ved in runof f producti on ar e

dependent on a di ff erent vari abl e. A second rain gauge in the cat chm ents was then

i ncluded i n the study and an average between t he two rai nf al l stati ons was obtai ned.

4.1.3.1 The catchment of 603136

F or stat ion 603136 the rainfall st at i on 9558 was intr oduced. The new rai nfall was

obt ai ned by the f ol l owing sim pl e m at hem at ical operati on,

95319558 4.06.0 RRRtotal ×+×= Equ at io n 4 -1

where R total is the “new” rai nf all f or the cat chm ent. The coeff icients of 0.6 and 0. 4 wer e

sel ected to maxim ise the corr el ati on coef fi cient between annual str eam fl ow and annual

r ai nf al l . R 9 55 8 and R 9 53 1 ar e the annual rai nfall s at the gauges 9558 and 9531,

r espect i vely. All rainfal l values ar e measur ed in mm. The int roduct i on of the second

r ai n gauge incr eased the corr el ati on coef fi cient between annual rai nfall and annual

str eamf l ow f r om 0.63 t o 0.69, an i mpr ovem ent of 10% .



4.1.3.2 The catchment of 603190

T o im pr ove the corr elati on coef f icient for stati on 603190, rainfall gauge stati on 9558

was agai n used to obtain aver aged val ues.

95959558 5.05.0 RRRtotal ×+×= Equ at io n 4 -2

T he int r oduct ion of the second gauge incr eased the corr elati on coef f icient by just 5%

f rom 0. 65 to 0. 69. Once again, the coef fi ci ent s of 0. 5 wer e sel ected to achieve the

highest level of accur acy of the linear model. However, these cor rel at ion coeff i ci ent s

wer e st i ll l ow compared wit h the accuracy achi eved for other catchm ent s.

4.1.4 Transformation of variables

When the unexpl ai ned var i at ion is lar ge, one or mor e of the var iabl e should be

t ransfor med (Johnson, Bhatt achar yya, 1996). Ther e wer e two di ff er ent transf or mat ions,

cases A and B t hat wer e per form ed on the annual rai nf al l dat a f or t he ni ne catchment s.

T he l inear m odel assum es

baxy += , where y is annual st reamf low in GL , x is the annual rainf al l in mm and a

and b ar e two cat chm ent dependent par am et ers. If the rel at ionship between x and y is

non-l inear . It can be for ced to fi t a linear rel ati onshi p by the tr ansfor mati on of one

var iabl e by a sim pl e m at hem at ical operati on.

4.1.4.1 Case A:

T he fir st tr ansform ati on scenar i o assum ed that st ream fl ow was dependent on the squar e

of the rai nf all . Therefor e, f or the dat a to fi t a l inear m odel then x must be t he square root

of the annual r ai nf all .

baxy += , where y is t he st reamf low and annualRx =

Fig ure 4 -3 : A s ch ema tic rep res en ta t io n of th e s qu are t ra ns f orma tio n



4.1.4.2 Case B:

T he second tr ansf or m at ion scenar io assumed that str eamf l ow was exponenti all y

dependent on annual rainf al l. Ther ef ore, for t he data t o fit a li near model t hen x must be

t he nat ural log of the annual r ainfal l.

baxy += , where y is t he annual st reamf low and ( )annualRx ln=

Fig ure 4 -4 : A S ch ema tic rep res en ta t io n of a lo g t ra ns f orm

T he cor r el at i on coef fi ci ent s for the two tr ansfor mati on of vari ables cases ar e shown in

t able 4. 3. The corr elati on coef f icients of the li near model on the raw data have been

i ncluded f or compar i son pur poses.

Tab le 4 -3 : correlat io n coeff icien t s fo r t he t w o ca ses o f t ra ns fo rming th e an n ua l rainf all

S tr eamf l ow

gauge

P er iod of

r ecor d

Cat chment

area

r ( Li near

Model )

r ( Case A) r ( case B)

612002 1970- 2002 2 538 0.86 0.84 0.83

612152 1962- 1982 210 0.89 0.87 0.85

603136 1961- 2001 532 0.69 0.69 0.70

603190 1964- 2001 57 0.69 0.69 0.68

605012 1954- 2001 4520 0.77 0.73 0.72

606185 1965- 1998 407 0.88 0.81 0.80

606195 1965- 1998 250 0.85 0.80 0.79

607144 1962- 2001 461 0.82 0.82 0.82

610001 1971- 2002 438 0.86 0.86 0.85

T he appl icat i on of thi s model was unsuccessf ul wi th the corr elati on coef f icient s

r em ai ni ng the sam e or act uall y reduci ng. There was only one case that resul ted in a



m ar gi nal impr ovem ent in the cor r el at i on coef fi ci ent , and that was for the log

t ransfor mati on in catchm ent 603136 which im proved the corr el ati on coef fi cient by just

1.5% fr om 0. 69 to 0. 7.

4.1.5 Multiple Regression

Aft er at tempt s to tr ansf orm the annual rainf al l fai led it was conjectured that a mor e

accur at e predicti on of st ream fl ow could be obt ai ned if the response of annual

str eamf l ow was model led to be dependent on mor e det ai led feat ur es of the annual

r ai nf al l . That is, the infl uences of ot her var iables wer e consi dered. Mul ti pl e regressi on

r ef er s to the model of a relati onshi p where the response depends on two or more

predi ct or var iabl es accor di ng t o equati on 4. 3 (Johnson, Bhat t acharyya, 1996).

nno xxxY ββββ ++++= ...2211 Equ at io n 4 -3

F or thi s study Y is the annual st reamf low for the cat chment and xi ar e var iabl es such as

annual rai nf all , wi nter rai nf al l , sum mer rai nf al l , aver age intensit y of the rai nfall ,

num ber of rai n days and maxim um dail y rai nf all . _ ar e the par ameters obtai ned fr om

t he appl icat i on of mul ti ple r egr essi on anal ysi s to the dat a.

T he next appr oach was to undert ake a seri es of mult iple regr ession analyses. The

pur pose of this was to hi ghli ght var i ous charact eri st ics of the rai nfall such as the total

vol um e, but also var iabl es that gave an indi cati on of the var iance of the rai nf all duri ng

t he year . Mul ti pl e regressi on models were developed wit h var i ous com bi nat ions of the

above vari abl es. The var i ables wer e chosen because each is im port ant in the process of

r unof f gener ati on.

Annual rai nf all for m s par t of the wat er bal ance of the region. Aver age intensit y is

cal culat ed by the total annual rai nf all divi ded by the num ber of rai n days. The number

of rain days is indi cati ve of the spr ead of rainf al l thr oughout the year . If a region has a

standar d annual rai nfall but a high num ber of rai n days, then the catchm ent woul d

produce less runoff than for the sam e cat chm ent having the same rai nfall occurr i ng on

f ewer rain days. Maxim um dail y rai nf all provides an indi cati on of the vol um e of

r ai nf al l occurr ing in ext reme events. Ext rem e event s have the highest st r eamf low

produci ng capacit y.



Var iabl es that measure the vari ance of rainf al l dur ing the year may be used to indicate

how much of the rai nfall is act ual ly pr oduci ng runoff . If the average int ensi ty of a year

i s low, then it suggests that t he ei t her there was a low rai nfall for that part i cular year, or

t here was a average rainf al l for a part icul ar year but onl y low int ensit i es occurr ed on

i ndividual days. In such an aver age year there woul d be fewer event s that would cause

t he maxi mum inf il tr ati on capaci t y to be exceeded and hence annual st ream f low would

be low.

T he mor e successf ul resul ts of these varying models f or just three of the cat chm ents ar e

shown in tabl e 4. 4 in the for m of the cor rel at ion coeff i ci ent s obtai ned bet ween the

predi ct ed and the actual st ream f low. The use of a mul ti ple regr essi ve model result ed in

cor relat ion coeff ici ent to 0. 86 - 0. 90, yiel di ng im pr ovement s at most of 3% .

4.1.5.1 Case A:

2211 xxQ ββ += Equ at io n 4 -4

4.1.5.2 Case B:

3311 xxQ ββ += Equ at io n 4 -5

4.1.5.3 Case C:

44332211 xxxxQ ββββ +++= Equ at io n 4 -6

Q: Annual str eam fl ow [ GL]

x1 : Annual rai nf all [ mm ]

x2 : Average intensi ty [ m m/ day]

x3 : Num ber of rai n days [days]

x4 : Maxim um dail y r ai nf all [ m m]

Tab le 4 -4 : correlat io n coeff icien t s fo r multip le reg res sio n an aly sis

S tr eamf l ow

gauge

P er iod of

r ecor d

Cat chment

area

r ( case A) r ( case B) r ( case C)

612002 1970- 2002 0.86 0.86 0.86

612152 1962- 1982 0.90 0.90 0.93

605012 1954- 2001 4520 0.77 0.77 0.79



4.1.6 Seasonal Modification

I n sect i on 4. 1. 4 the x vari abl e was transf or med by usi ng the log funct i on or the square

r oot funct ion t hat approxim at ed annual st reamf low t o be exponenti al l y rel at ed t o annual

r ai nf al l or rel at ed to the squar e of the rai nf al l . The next approach taken was to once

again modi fy the x var iable. The annual rai nfall was seasonal ly modi fi ed by separati ng

t he annual r ainfall into seasonal com ponent s.

Fig ure 4 -5 : A nn ua l s treamflow vers u s ra in f all, high lig ht in g y ea rs with simila r rain fa lls b ut d if feren t st rea mf lo w .

T he annual st ream fl ow dat a was once again pl ot ted against the annual rai nfall , Figur e

4.5. The rai nfall fr om year s that had sim il ar rai nf al l, but vastl y dif fer ent st r eamf l ows

wer e analysed at sm all er ti mescales in at tem pt to under stand how the dist ri buti on of

r ai nf al l wit hin the year ef fect s the annual st reamf low. That is how the spr ead of the

r ai nf al l dur i ng the year af fect ed the vol um e of str eamf l ow that t he catchment pr oduced.

Monthly values were anal ysed over a 24 mont h per i od for both the act ual year and the

previ ous year .

F or the catchment of stat ion 612002 the year s 1976 and 1985 wer e ident if i ed as years

t hat showed sim il ar volum es of rai nf all and very di ff er ent st ream fl ows. 1976 was a dr y

year wi t h a low str eam fl ow of 35GL though it received 752m m rai nf al l . 1985 recei ved

764mm , just 12m m mor e rai nf al l, but the sam e cat chm ent produced 115GL st r eamf low.



T hat is, a di ff er ence of 12mm of rai nfall result ed in a st reamf low dif fer ence of 80GL .

S im il ar di ff erences were al so observed in the years 1983 and 1988. 1983 was

signi fi cantl y wet ter t han expect ed, producing 285GL str eam fl ow fr om 1040m m rainf al l.

But in 1988 whi ch rainfal l was sli ght ly higher , 1066m m, but produced onl y 198GL

str eamf l ow.

When the m ont hl y dat a wer e exam i ned it appeared that a possi ble cont ri but ing factor to

t hese si gnif i cant di ff er ences in str eam fl ow was the vol ume of sum mer rai nfall that the

cat chment recei ved. Mont hly rai nfall and st r eamf l ow are shown in Fi gur e 4.6 for the

years of 1982-1983 and t he year s 1987-1988 where the ef f ect of the rai nf all r eceived by

t he cat chm ent in the sum m er mont hs can be observed. Whi l st the volum e of wi nt er

r ai nf al l received in the year s 1988 and 1983 are compar abl e, rainfal l recei ved in the

sum mer months was si gnif i cant ly di ff erent . 1988 has two mont hs duri ng the sum mer

when there was negl i gi bl e rai nf all . Thi s would lead to the catchm ent dryi ng out

signi fi cantl y so that the fir st rains of the next winter woul d then be taken up in

cat chment wet ti ng, so that li tt l e wat er was avai l able f or pr oduci ng runof f.



Fig ure 4 -6 : M on th ly ra inf all a nd s t reamflo w va lues fo r t he years o f 19 8 2 to 1 9 83 a n d 19 87 to 1 9 88 .

T hi s analysi s i nt roduced the concept of t he seasonal dependence of the data. The annual

r ai nf al l was separat ed into seasonal component s of summ er and winter . Sum mer was

def ined to be the fi rst 5 months of the year , January to May, and af ter som e it erati ons

winter was defi ned to be the months of June to Sept em ber . T he three mont hs of

Oct ober to December di d not appear t o eff ect t he assumpt ions made f or the m odel .

T wo m et hods of seasonal modif icati on were t hen i nvest igated:

i. T he m odi fi cat ion of the annual rai nf all wit h t he summ er defi cit and

ii. T he m odi fi cat ion of the winter rai nf all wit h t he summ er defi cit .

T he sum m er defi ci t is the sum mer rai nfall for a par ti cul ar year , mi nus the mean of the

sum mer rai nf all over t he peri od of r ecord.



F or the foll owi ng two cases, case A and case B, QA is the annual st reamf low in GL , R A

i s the annual rai nf all in mm and ( RS – RS ) is the summ er defi cit of the rainfal l in mm .

T hese seasonal modi f icat i ons ar e not repr esent at i ve of act ual cat chm ent processes, but

t hey scale the data relat ive to one another to enable a more accurat e model to be

produced. The r esul t s of the seasonal m odif i cati on of t he dat a ar e shown in t abl e 4. 5

T he two cases ill ust rate di ff er i ng form s of seasonal modif icati on of the annual rainf al l

dat a. The li near model was devel oped wi th the new rai nf all inputs. The model fi t s the

f or m

baRQ M += Equ at io n 4 -7

wher e R M is the seasonall y modif i ed rai nfall data as def i ned by the two cases A and B

bel ow.

4.1.6.1 Case A:

( )SSAM RRRR −+= Equ at io n 4 -8

Case A int roduces t he concept of a t ypi cal sum mer drying per i od.

4.1.6.2 Case B:

( )SSWM RRRR −+= Equ at io n 4 -9

Case B goes fur ther than case A and includes the two seasonal characteri sti cs of annual

r ai nf al l that have the most signif icant eff ect on the annual runoff , sum m er and wi nt er

r ai nf al l . Wi nter rai nf al l is the most signi f icant contr i butor to runof f in term s of vol um e

produced and result s from the east war d passage of successi ve cold fr onts over the

south-west . These fr ontal system s resul t in rainf al l event s of hi gh intensi ty. Dur ing the

winter months the catchm ent has hi gh antecedent moi st ur e condit ions and as a

consequence small am ount s of rai nf al l wil l resul t i n the producti on of st ream fl ow. S i nce

m ost of the rai nf al l events are of hi gh int ensit y there is an increase in the pr obabi li ty of

exceedi ng the m axim um inf il tr at i on capaci ty.



Tab le 4 -5 : R es ult s of th e sea so na l mod if ica tio n of t h e ra inf all d at a

S tr eamf l ow

gauge

P er iod of

r ecor d

Cat chment

area

Cor relat ion

coeff ici ent (li near

m odel )

Cor relat ion

coeff ici ent

( case A)

Cor relat ion

coeff ici ent

( case B)

612002 1970- 2002 2538 0.86 0.80 0.87

612152 1962- 1982 210 0.89 0.82 0.91

603136 1961- 2001 532 0.69 0.61 0.81

603190 1964- 2001 57 0.69 0.60 0.75

605012 1954- 2001 4520 0.77 0.66 0.81

606185 1965- 1998 407 0.88 0.80 0.88

606195 1965- 1998 250 0.85 0.83 0.87

607144 1962- 2001 461 0.82 0.74 0.87

610001 1971- 2002 438 0.86 0.80 0.89

T he seasonal modi fi cat ion of the annual dat a wit h the summ er defi ci t (case A) has a

negat ive i mpact on the success of the m odel . Case B, however , i ncreases the cor r el at i on

coeff ici ent in al l cases between 0% and 9%. For the cat chm ent of 603136 the seasonal

m odif icati on im pr oves the cor rel at ion coeff i ci ent by 17% .

4.1.7 Non-linear Fits

F igur e 4-7: L inear m odel appl i ed t o t he seasonall y modif ied dat a



Whi lst the i m pl em ent at ion of the seasonal ly modi f ied li near dat a mar gi nal ly r educes the

broad scat ter of poi nt s, it has a sm all eff ect on t he di st ri but ion of the dat a, as i s shown in

F igur e 4.8. The model st i ll fol l ows the pat t er n of under pr edi ct ion, over predi ct i on then

under pr edi ct i on wit h incr easi ng annual rainf al l. As a consequence the li near model was

abandoned and i t was deci ded to appl y a quadrati c f it t o the raw annual rai nf al l dat a and

t he seasonal l y modi f ied dat a.

4.1.7.1 Case A:

A quadr ati c fit was appl i ed t o the r aw annual rai nf al l and annual st ream f low dat a.

cbRaRQ ++= 2 Equ at io n 4 -1 0

where R is t he annual rainf al l in mm and Q is t he annual st reamf low m easur ed i n GL.

4.1.7.2 Case B:

A quadr ati c fit was appl i ed to the seasonal l y modif ied rai nf all and the raw annual

str eamf l ow data. The seasonal ly modi f ied dat a is defi ned as case B from 4.1.6.2 wher e

t he modi fi ed rainfal l was a functi on of the wi nt er rainf al l and the summ er rainf al l

def icit .

cbRaRQ MM ++= 2

Equ at io n 4 -1 1

where

( )SSWM RRRR −+= Equ at io n 4 -1 2



Tab le 4 -6 : The co rrela tion co ef ficient s f or t h e tw o q ua dra tic mo d els, ca se A an d cas e B

S tr eamf l ow

gauge

P er iod of

r ecor d

Cat chment

area

r ( li near model ) r ( case A) r ( case B)

612002 1970- 2002 2 538 0.86 0.71 0.93

612152 1962- 1982 210 0.89 0.92 0.91

603136 1961- 2001 532 0.69 0.70 0.83

603190 1964- 2001 57 0.69 0.69 0.79

605012 1954- 2001 4520 0.77 0.78 0.82

606185 1965- 1998 407 0.88 0.90 0.91

606195 1965- 1998 250 0.85 0.85 0.87

607144 1962- 2001 461 0.82 0.82 0.89

610001 1971- 2002 438 0.86 0.87 0.90

T he result s for these two cases ar e displ ayed in tabl e 4.6. Case B has pr oduced posi t ive

r esul ts wi th si gnif i cant im pr ovement s on the cor r el at ions fr om the linear model and

f rom the quadrati c fit appl ied to the raw data. A plot of the raw data and the sim ul ated

str eamf l ow data usi ng case B is di spl ayed in Figure 4.9. The use of this model has

r educed the pat terns in the resi dual s and the pat terns of overpredi cti on and

under pr edi ct i on.

F igur e 4-8: S im ul ated str eamf l ow data f or the cat chment of 612002.



4.2 The m odel

T he m odel is in t he form

cbRaRQ MM ++= 2Equ at io n 4 -1 3

where R M is t he seasonall y adj ust ed r ai nfall accordi ng t o

( )SSWM RRRR −+= Equ at io n 4 -1 4

where QA is the annual st reamf low in GL , R W is t he wi nt er rainf al l in mm and ( RS – RS )

i s the sum mer def ici t of the rai nf al l i n mm .

T he coef fi ci ent s a, b, and c ar e cat chm ent dependent par am et ers, resul ti ng fr om the

appli cat ion of the quadr ati c model , and are di spl ayed i n t abl e 4. 7

Tab le 4 -7 : The eq ua tio n of t h e mo d el f or th e n in e ca t ch men ts

S tr eamf l ow

gauge

P er iod of

r ecor d

Cat chment

area

T he m odel

612002 1970- 2002 2 538 124576.0001.0 2 +−= MM RRQ

612152 1962- 1982 210 5045.00001.0 2 ++= MM RRQ

603136 1961- 2001 532 33184.00004.0 2 +−= MM RRQ

603190 1964- 2001 57 8045.00001.0 2 +−= MM RRQ

605012 1954- 2001 4520 30150.00009.0 2 −+= MM RRQ

606185 1965- 1998 407 34092.00002.0 2 +−= MM RRQ

606195 1965- 1998 250 5039.0 −= MRQ

607144 1962- 2001 461 18078.00001.0 2 −+= MM RRQ

610001 1971- 2002 438 34066.00002.0 2 +−= MM RRQ



4.2.1 Calibration of the model

T he dat a from the catchm ent 612002 repr esent s a per iod of 33 year s from 1970- 2002.

T he m odel was cal ibr at ed over the fi r st 26 years then veri fi ed for the foll owing 7 year s.

T he cor r el at i on coef fi ci ent bet ween the predicted str eam fl ow data and the act ual

str eamf l ow data was 0. 96. However, cali br at i on adjust ment showed som e sensi ti vi t y. If

t he model was cal ibr at ed over the fi r st 27 years and ver if ied over the next 6 year s, then

t he cor r el at i on coef fi ci ent bet ween the predicted and the act ual st r eamf l ow dat a was

r educed to 0. 82.

A f ur ther cal ibrati on was car ried out f or a stat i on t hat has recordi ngs bef or e the 1970s to

t est that the cal ibr at ion of the model was not sensit ive to the cli m at ic shif t of the mid

1970s. Stati on 605012 was sel ect ed due to the long peri od of record spanning 1954-

2001. The model was cali brated for the fi rst 32 years, 1954- 1985, of record and

i ncluded the decade af ter the cl im at i c shif t of 1975. The st r eamf low dat a cal cul at ed

usi ng the model was veri f ied against the act ual str eamf l ow data producing a cor r el at i on

coeff ici ent of 0. 88

4.3 A pplication of the m odel

T he developed model is in a convenient form to make predicti ons of the ef fect of

cli mate change on st ream f low. Most pr oj ecti ons on changes to rainfal l in the south-west

of West ern Aust rali a are in the form of the changes to rai nf all in each of the seasons.

T hese pr oj ect ions ni cely fi t the m odel, whi ch has i nput s of sum mer and wi nt er r ainfal l.

Cli mate change pr oj ect ions for the sout h- west indicat e that sum mer rai nf all wil l change

l it tl e, but that si gni fi cant decreases wi ll be experi enced in winter rai nfall wi th a

r educti on of up t o 30% by t he year 2030, Fi gur e 2.3.

T he model is in the form cbRaRQ MM ++= 2 , where ( )SSWM RRRR −+= . To

cal culat e the eff ect of cli mate change on st ream f low the equati on for st r eamf low

r em ai ns the sam e and it is just the seasonal ly modi fi ed rainf al l input that is changed

accor di ng to cl im at e change proj ecti ons. The rai nfall input to the model to predict

str eamf l ow af ter cl i mate change is defi ned by equat ion 4.15.



( )SSWM RRRR −+×= 7.0 Equ at io n 4 -1 5

T he wint er rainfall is mult ipli ed by 0. 7 as wi th cl im at e change it is expected to decli ne

by 30%.

4.3.1 The effect on streamflow of climate change in the south-west

T he model was cal ibr at ed over the ent ir e per iod of recor d for the foll owi ng stat ions.

T hen the aver age annual str eamf l ow af ter 1975 was com par ed wi th the mean annual

str eamf l ow pr oj ected wit h the reduced rai nf all inputs due to cl im at e change. Table 4. 8

shows t he expected changes to st ream f low if wi nt er rainf al l decli nes by 30%

Tab le 4 -8 : C ha ng es to mean a n nu al st reamf lo w a ft er a 30 % red uctio n in wint er ra in f all du e t oclima te ch an g e.

S tr eamf l ow

gauge

Cat chment

area

Mean annual

str eamf l ow

( post 1972)

Mean annual

str eamf l ow ( post

cli mate change)

% decli ne in

str eamf l ow

612002 2 538 109 53 52

612152 210 55 36 36

603136 532 30 13 54

603190 57 5 3 41

605012 4520 156 73 54

606185 407 64 33 48

606195 250 44 23 48

607144 461 29 13 55

610001 438 91 53 42

All of the catchm ent s show comparabl e decreases in the order of 50% in response to a

30% reduct ion in wi nter rai nf al l . The one except i on to thi s is the cat chm ent of

str eamf l ow st at ion 612152. However , the cat chm ent for 612152 only has dat a for 1962

t o1982. This means that the mean annual str eam fl ow calculated for the per iod af t er the



cli mati c shi f t of t he 1970s has only a few dat a poi nt s, not enough to make a meani ngf ul

predi ct i on, hence t he resul t can be discarded.

4.3.2 The effect of climate change on inflows to Perth dams

T he mai n dam s that suppl y wat er for the Int egr at ed Water Supply Syst em for Pert h are

S outh Dandal up, Nor t h Dandalup, Serpent ine, Wungong, Canni ng, Victor ia, Mundari ng

and Sti r li ng. The largest contr i buti on is fr om Serpenti ne Mai n Dam wit h a licensed

abstr act ion of 51GL per year. Coll ect ivel y the infl ow to these dams decr eased by 50%

aft er 1975 wi th a reduct i on from 338GL to 164GL as shown in Figur e 4.10. This

decrease r esult ed f r om a 20% reducti on in r ainfal l (Wat er Cor porati on, 2003).

Fig ure 4 -9 : A nn ua l s treamflow fo r maj or s u rf ace w at er so urces f or Pert h . (S ou rce: Wat erC orpo ra t io n)

T he rai nfall fr om Serpent ine was used to approxi m at e the precipit at i on occurr ing in the

cat chments for al l the major dam s for Per th’ s wat er supply. Thi s was because it is the

l ar gest cont r ibut or , and al so as i t is si tuated near to the Nor th and South Dandal up dams

whi ch coll ect ivel y provi de a si gni fi cant pr oport i on of the infl ow to Per t h dams. The

m odel was cal ibrated from 1910 to 1974 wi th the str eamf l ow data presented in Fi gur e

4.10 and the rainfal l fr om Serpent ine. The cor rel at ion coeff i ci ent bet ween the predi cted

str eamf l ow using the m odel and the actual st ream f low fr om 1910 to 1974 was 0. 89.



When the rai nfall reduct i on is model l ed as 20% , the model under pr edi ct s the reduct ion

i n the mean annual str eam fl ow. The model pr oject s a decr ease in mean annual

str eamf l ow of 331GL to 217GL, a decl i ne of 34% . However , the actual decr ease is

50% . To obtai n this decr ease, the model requir es a reducti on in the wi nt er rainf al l of

30% . Thi s could be because the rai nf all in the Serpenti ne cat chment , on average, is

higher than that exper ienced in the other catchm ent s for m aj or dams for Per th.

4.4 S um mary of the model

T he model devel oped in this study has provi ded a si mple and easy met hod by which to

predi ct the eff ect of cl i mate change on str eam fl ow. The model is in such a form that

cli mate change pr oj ect ions ar e easil y included. In a cat chment of 2 538km 2 the model

predi ct s the st ream f low from one rai nfall st at ion wit hi n the catchm ent at taining a

cor relat ion coeff ici ent of 0. 93.

S uch a model coul d have maj or im pl icati ons on the planni ng of wat er suppl ies in Pert h.

T hi s model wi ll hel p to rem ove the myst er y that sur rounds the issue of cl im at e change

and how it wi ll eff ect t he envi r onment of sout h- west Western Aust ral ia.

S im pl e empir i cal model s have been ignor ed in the past due to the hi ghl y com pl ex

nat ur e of bot h runof f gener at ion processes and cl im at e change. This st udy has pr oved

t hat a sim pl e, em pi r ical model can in fact be a power ful management tool . The ease of

i ts appl icat i on to cat chm ents across the south-west and the conveni ence in term s of the

i ncor por at ion of cl i mate pr oj ect ions make i t f avour able to pl anners and manager s.

T he model indicat es that wi th a 30% reducti on in wi nt er rainf al l, the south-west of

Western Aust r al ia wi ll experi ence a cor responding decrease i n mean annual str eam fl ow

of appr oxi mat el y 50% .

C ha pter 5: Ad va ntag e s an d L imita tion s o f th e Mod e l


5 A dv anta ges a nd Limit at ions of t he modelT he maj or advantage of this model is the si m pl ici ty of the inputs and the ease of

project i ons, both shor t- t er m and t he ef fect of cl im at e change i n the l onger t er m . Si m pl e,

empir ical model s such as these have been neglect ed in the past as powerf ul pr edi ct ion

t ools. Thi s was because it was bel ieved that since st reamf low resul t s fr om the com pl ex

i nt er act ion of the runof f gener ati on pr ocesses, it coul d not be repr oduced by such a

sim pl e model .

5.1 S patial and Tem poral V ar iability

Rai nf al l exhi bi ts both spat ial and tempor al vari ati on. Tim escal es ar e im por tant in the

quant if i cati on of this vari at ion. For exampl e, two rainf al l gauges sit uat ed near each

other coul d have vastl y dif feri ng recor di ngs on a dai ly ti mescale, however, aver aged

over a year it is li kely that the rai nf al l gauges would recor d si mi l ar annual rainfal l. By

i ncreasi ng t he ti mescale of i nt erest , t he spat ial var iat ion has been r educed. T he tem poral

scales of rai nf al l are i m port ant i n the processes of runof f gener at i on. Dur ing the passage

of a st orm over a rainfal l gauge, the int ensit y wil l increase unt il it reaches the peak

i nt ensi t y, t hen i t wil l decrease as the stor m passes. S t ream f low onl y resul ts f r om r ainfall

or suff i ci ent int ensit y such that the maxim um inf il tr at i on capaci ty is exceeded unless

t he soi l i s sat ur at ed and sat ur ati on excess over l and fl ow occur s.

Whi lst not af fect ing the development of the model for any cat chment s, spati al vari at i on

of rainf al l and rai nfall changes becomes im por tant in the appli cati on of the model to

predi ct futur e mean annual st reamf low aft er the eff ect of cl i mate change. No local ised

studi es have been conduct ed on the ef fect of cli m at e change in the south- west of

Western Aust r al ia. Regional studies have pr oject ed that wi nt er rainf al l wil l decrease

uni form l y acr oss the sout h- west . Exam inat ion of rai nf al l records that span the cli mat ic

shi ft of the mi d 1970s show that cli m at e change is not aff ect ing the sout h- west

uni form l y and that the sout h coast has exper ienced a much less noti ceabl e decli ne in

winter rai nf all . In or der to pr ovi de a more accur at e pi cture of fut ure water suppl ies in

Western Aust r al ia, it is necessary to det er m ine how all of the st reamf low wil l be

aff ected by cli mate throughout the st at e. Whil st models such as thi s coul d pl ay a key

r ol e in managem ent of wat er suppli es in the futur e, mor e accurate cl im at e change

sim ul at i ons are r equir ed to i nput int o the model for st r eamf l ow sim ulati ons.



Jot hi tyangkoon et al . 2000 developed a st ochasti c model of space ti me rainfal l for the

southwest region of West ern Aust rali a as the empi ri cal featur es associ at ed wi th

obser ved rai nfall , such as temporal int er mi t tency and spat ial pat chi ness ar e captured

m uch mor e readi ly by these model s (Gupt a and Waym ir e, 1993; Tessi er et al ., 1993) .

T hi s model was devel oped wi th the ai m to ai d fur t her st udi es into the pr oduct ion of

r unof f and how it responds to tempor al vari ati ons in rai nf al l (Jothi tyangkoon et al ,

2000) .

T he model devel oped in this study was onl y int er est ed in capt ur ing the si gnat ur e of

annual str eam fl ow. Annual str eam fl ow is an impor t ant indicat or of future water

suppl ies. For model l ing pur poses, the advant age of investi gat ing st r eamf l ow at thi s

scale is that small scal e fluct uat ions in rainfal l ar e insignif icant . To accurat el y model

annual str eam fl ow it was found that it was onl y necessar y to reduce rainf al l to a

seasonal t im escal e.

5.1.1 Future Projections

T here ar e al so li mi t at ions wi th the predi ct i ve power of this model. The model is

cal culat ed fr om appl yi ng a quadr at ic fi t to the annual str eam fl ow and the seasonal ly

m odif ied rai nfall data. The result ing model has par am et ers that are hi ghl y vari abl e

bet ween the cat chment and assum ed to be a functi on of soil and landscape

charact eri st i cs.

As the cli mat e changes, land use wil l change accordingl y. Wi t h a mor e ar i d cl im ate,

nat ur al veget at ion may change to be sim il ar to that of cat chm ents furt her inl and, whi ch

r ecei ve a lower aver age annual rai nf all . For exam pl e, if the forest in the Coll i e

cat chment recei ves less rai nf al l annual ly, it may evolve to becom e mor e like the nat ural

veget at i on furt her inl and whi ch curr ent ly experi ence the reduced rai nf al l that is

expected for Coll ie Ri ver Basin in the futur e. This is the pr inci pl e of subst it uti ng ti me

f or space.

S ince the model cont ai ns such a hi gh dependency on the characteri st i cs of the

cat chment, it is li kel y to be sensit i ve to such maj or changes occur r ing in the land uses

f or the catchment . Thi s sensi ti vit y in the par am eters used in the model wil l reduce the



predi ct i on power over ti m escales of signi fi cant land use change. Thi s dependency wil l

l im it the num ber of year s whi ch the model can pr oject in the future. If, however , the

decli ne in rainfall occur s as a sudden dr op as opposed to a conti nual decli ne, then the

par am et ers of t he m odel wil l st i ll hold and pr oj ect ions wi ll remain vali d.

5.2 Implications for planning

5.2.1 Climate change or climate variability

One of the issues faci ng water suppl y planners is the issue of cl im ate change agai nst

nat ur al cl im ate var i abil i ty. Is the dry per i od t hat i s cur rentl y occur ri ng in the south-west

due t o a change i n cli mat e as a resul t of t he enhanced greenhouse ef fect , or is it j ust par t

of a decadal cycl e of nat ur al vari abi li ty?

T here ar e recent st udi es that suggest that the decl ine in wi nter rai nf al l is due to nei ther

of these, but the incr easing at m ospheri c pol luti on due to ant hr opogeni c act ivit i es. Such

studi es indi cat e that pol luti on has lead to changes in cloud form at i on and is hence

eff ecti ng weather patt er ns across Austr al ia. The Water Cor por at ion of Western

Austr al i a cl aim s that the drop in wi nter rai nf al l is due to nat ur al cl im ate var i abil i ty and

t hat ot her fact or s, such as land use changes and local air poll ut ion are unli kel y to be

m aj or cont ri but or s since rainfal l decli nes are recorded just as str ongly at the Rott nest

I sl and Light house as they are in other part s of Western Aust r al ia. There is lit t le

sustained local ised ai r pol luti on at Rott nest as it i s 16 km of t he coast of Per th.

T he model in this st udy has been produced independent ly of this debate. It ai med to

predi ct the eff ect of a change in cl i mate due to st ream f low. Whil st the reasons for the

decli ne in wi nt er rainfal l in the mi d 1970s ar e uncer tai n, the major it y of the studi es

project that si mi lar decl ines should be expect ed in the futur e. The model is insensi t ive

t o the causes of the changes to cl im ate and requi res onl y pr oject ed changes to sum mer

and wint er r ainfall .

5.2.2 Water Planning

Wat er pl anni ng in West er n Austr ali a is under cont roll ed by the Water Cor por at ion.

P lanning consider s not just sour ce developm ent , but also dem and management and

opt ions for reuse.



Cur rent l y there is a gap between the academ i c research on the eff ect s of cl im at e change

and the im pl ement at i on of these st udi es and pr oj ect ions into managem ent plans. Thi s

m odel is a si mple model that is adapt able to dif f er ing cat chm ents and cl i mate change

project i ons.

5.2.2.1 Short-term

Wit hout the appli cat ion of the cli mat e change pr oject ions to this model, it is sti ll an

i mpor tant managem ent tool . As the cl i mate of sout h- west West ern Aust rali a becom es

dri er ther e are incr easi ng water rest ri ct ions bei ng placed on wat er user s. A power ful

aspect of thi s model for managem ent pur poses lies in the fact that it onl y requi res

r ai nf al l unt i l the month of Sept em ber bef or e it can cal cul at e the expect ed st reamf low

f or the year . Thi s means that the requi red restr i ct ions for the fol l owing year could be

cal culat ed earl ier such that the i mpact on resident s is not so marked.

5.2.2.2 Long-term

T hi s model has been used to cal cul at e the expect ed decl i ne in mean annual str eam fl ow

f rom the proj ected changes in wi nt er rainfal l. It indicates that a 50% reduct ion in

str eamf l ow wi ll occur fr om a 30% reduct ion in wi nter rai nf al l . Such a resul t has huge

i mpli cat ions both f or the envir onm ent and f or wat er sour ce pl anni ng.

Cur rent l y there is great uncert ainty in the quant if icat i on of wat er suppl y in the fut ur e.

Wat er demand issues ar e easier to quant if y and is a functi on of populati on gr owt h and

m or e ef f icient water reuse schem es. To adequat el y plan for water suppl y in the fut ur e

confi dent est im at es the eff ect of cl i mate change on str eam fl ow ar e necessar y. Wi th

quant if i able data on the ef fect s on str eamf l ow, regions can be ident if ied that wil l

str uggl e wit h cli mat e change and incr easi ng demand and int er venti on pl ans can be

i mplemented ear li er . It is al so possi bl e that wi t h the increasi ng ur bani sat ion trend and

t he spat ial var iabi l it y inher ent wit h cli mat e change that som e regi ons wi ll be ident i fi ed

as havi ng a sur pl us of water. S pecif i c pr edi ct ions such as t hese, however , requi re hi ghly

l ocal ised pr oject ions of cl im at e change on winter r ai nf all .

C ha pter 6: C o nc lu sio ns


6 C onclus ionsT hi s st udy has pr oduced a sim pl e, em pir ical model whi ch is a powerf ul tool for wat er

suppl y management purposes. I t is a quadr at i c fi t appli ed to seasonall y modif ied r ai nfall

dat a, and has t he f orm

cbRaRQ MM ++= 2 ,

where Q is the annual st reamf low in GL , and R M is the seasonall y modif i ed rai nfall in

m m, and is defi ned by

( )SSWM RRRR −+=

where R W i s the winter rai nf all , R s is t he summ er rainf al l and SR is t he aver age sum m er

r ai nf al l aver aged over t he peri od of record.

T he eff ect of cli mat e change on st reamf low is easil y cal culat ed by the incorpor ati on of

cli mate change pr oj ect ions on r ainfal l into the equat ion for the seasonal m odif i cati on of

t he rai nfall data. A decl ine in wi nt er rainf al l of 30% is pr oject ed to result in an average

decli ne in st ream fl ow of 50%. Such a reduct i on wi ll have maj or im pl i cati ons for the

envir onm ent and wat er suppl y in West ern Aust rali a.

C ha pter 7: R e fe re nc e s


7 R ef er enc es

Abdul , A., Gi ll ham, R. 1989, F ield St udies on the eff ect s of the capil lary fri nge on

streamf l ow generati on, I nsti t ut e for Groundwat er Resear ch, Uni ver si ty of

Wat er loo, Ont ar io

Austr al i an Gr eenhouse Of f ice (AGO) , 2002b, P ot enti al Cl i mate Change impact s on

A ustral i a, Com monweal t h of Aust ral ia [ onl ine] , Avail abl e from

<ht tp:/ / www. greenhouse.gov. au/science/i mpact s/ overview/ pubs/ overview3. pdf >

[ 31 Jul 2003] .

Atkinson, S. , Woods, R., Si vapal an, M. 2002, Cli mate and landscape control s on wat er

bal ance model compl exi ty over changi ng ti mescales, Wat er Resources Resear ch,

vol 38, No 12, 1314.

Bat e, P . , 2002, Cli mate change or cl imat e variabil it y, cent ral A ust rali a, Bur eau of

Met eorol ogy, Nort her n Ter ri tory Regi onal Of f ice [pr esent at ion]

Bat es, B., Char les, S. , Hughes, J. 1998, Stochast ic downscal i ng of numeri cal cli mate

model si mulat ions, E nvir onm ent al Modell ing & S of t ware, vol . 13, pp. 325 – 331

Bonel l, M. , 1993. P rogress i n the understandi ng of runoff generati on dynamics in

f orests, Journal of Hydr ol ogy, 150, 217-275.

Bradshaw, M. Weaver , R. 1993, P hysi cal Geography: An i ntroduct ion to Eart h

E nvironments. Mosby, USA

Bramm er , D., McDonnell , J., 1996. A n evol ving percept ual model of hi ll slope f l ow at

t he M iami cat chment , I n: Anderson, M.G. , Br ooks, S . M. ( E ds.) , Advances i n

Hil lslope Hydrology. Wil ey, New York, pp. 35–60.

Centr e for Water Research. (undated) , L arge Scal e Cat chment Model ( LA SCA M) ,

[ onli ne] , Centr e for Wat er Research, Avai lable f r om :



<ht tp:/ / www2. cwr. uwa.edu. au/~tt f admi n/m odel / lascam/ lascam. ht m l> [ 17 Sep

2003]

Chow, V. , Mai dement R. , Mays, L . 1988, A ppli ed Hydrology, McGraw Hil l , New Yor k

CSI RO, 2001, Cli mate Change: P roj ecti ons f or Aust ral ia, [ onli ne] CS IRO, Avai lable

f rom, <htt p: / /www.dar. csi ro.au/ publi cat ions/ pr oj ect ions2001. pdf >, [ 25 Jul 2003]

De Souza, M. , 2002, I nf luences on t he decl ine i n wi nter rai nf al l i n the sout h- west of

W estern Aust ral ia, under graduate t hesis at Centr e f or Water Resear ch

( unpubl i shed)

E lsenbeer, H. , West , A., Bonnel l , M. , 1994, Hydraul i c pat hways and st ormf low

hydrochemi st ry at Sout h Creek, northeast Queensl and, Journal of Hydr ol ogy, 162,

1-21.

F ar mer, D. , Sivapal an, M. , Jothi tyangkoon, C., 2003, Cli mate, soi l , and veget ati on

control s upon the vari abi li ty of wat er balance in temperat e and semi -ari d

l andscapes: Downward approach to wat er balance anal ysis, Wat er Resources

Research, Vol 39, No 2

F et ter, C. 2001, A ppli ed Hydrogeol ogy, P rent i ce Hall I nc, New Jersey

Gupta, V., Wyam ir e, E. , 1993, A stati sti cal analysis of mesoscal e rai nf al l as a random

cascade, Journal of Appl ied Met eor ol ogy, 32(2) , 251- 267

Hor nber ger , G., et al . 1998, E lement s of Physi cal Hydrol ogy, T he Johns Hopki ns

Uni versi ty P r ess, US A

Hughes, J. P and Gut t or p, P. 1999, A non-homogenous hi dden Markov model for

preci pi t at ion occurrence, Appli ed St ati st ics, vol . 48, no. 1, pp. 15-30



I nt er nat ional Panel on Cl im at e Change (IP CC) . 2001, Cli mate Change 2001: Working

group II ; Impacts, Adapt ati on and Vul nerabi l it y, [onli ne] , Avail abl e fr om

<ht tp:/ / www. gir da.no/cli m at e/ ipcc_tar /wg2/i ndex. htm > [12 S ep 2003].

I OCI, 2002, Cli mate Vari abi li ty and Change in the South- West , Indian Ocean Cl im ate

I ni ti at i ve

Kir chner , J. , A doubl e paradox in catchment hydrol ogy and geochemistry, Hydrol ogical

P rocesses, 17, 871- 874, 2003

L egesse, D, Val let- Coulom ba, C, Gassea, F 2003, Hydrological response of a

cat chment to cl imat e and land use changes in Tropical Af ri ca: case study Sout h

Central Et hi opi a, Journal of Hydr ol ogy, Vol . 275 pp. 67-85

L insl ey, R., Kohl er , M., Paul hus, J. , 1982, Hydrology for Engineers, McGraw Hil l Inc,

S ingapor e

Johnson, R. Bhatt achar yya, G. 1996, St at ist ics: Princi ples and Met hods, John Wi l ey

and S ons I nc, New York.

Jot hi tyangkoon, C., Si vapal an, M. and Far mer , D. 2001, P rocess cont rol s of water

bal ance vari abi li ty in a large semi- ari d cat chment: downward approach to

hydrological model devel opment, Journal of Hydr ol ogy, vol . 254, pp 174- 198.

Jot hi tyangkoon, C., Si vapal an, M., Vi ney, N. , 2000, Test s of a space-t i me model of

dai ly rainfal l in sout h- western Aust ral ia based on nonhomogenous random

cascades, Water Resour ces Research, Vol 36, 267- 284.

Mar yl and Depart ment of Natural Resour ces (29 May 2003), R ivers and St reams:

Def init i ons, [onl ine], Avail abl e fr om:

<ht tp:/ / www. dnr .stat e. md. us/str eam s/ 101/def i ni ti ons.htm l > [ 3 S eptem ber 2003] .



McBri de, J. Nicholl s, N. , 1983. Seasonal rel ati onshi ps between Australi an rainfal l and

t he Sout hern Osci ll ati on, Mon. Weath. Rev., 111, 1998-2004.

McDonnel l, J. 1990, A R at ional e for old water discharge through macropores in a

steep, humid catchment , Water Resour ces Research. 26, 2821- 2832.

Naj af i, M. 2003, W at ershed model li ng of rainfall excess transformati on i nto runoff ,

Journal of Hydr ol ogy, 270, 273- 281.

Nicholl s, N. Cham ber s, L . Haylock, M. F reder iksen, C. Jones, D. Drosdowsky, W.

1999, Cli mate Vari abi li ty and Predi ct abi li t y for South- West W est ern A ustrali a,

Bur eau of Met eorology Research Centr e, Melbour ne

Oki and Lanae Labor atory. (2003) , Hydrology 2020 – Technol ogi es, [ onli ne] ,

Uni versi ty of T okyo, Avai labl e from

<ht tp:/ / hydr o.i is.u- tokyo.ac. jp/ H2020/2003/ Hydrol ogy2020_t echnology2003-

0701. pdf > [15 Aug 2003]

P earm an, G., and Hennessy, K., 2002, Cli mate science: what do we know? In: Living

wit h cl i mate change, a nat ional conf er ence on cl im at e change impact s and

adapt at i on: conference papers [ onl ine], Aust rali an Academy of S ci ences. Canberr a:

Austr al i an Gr eenhouse Of f ice, avai lable from

<ht tp:/ / www. dar .csi r o. au/ publ icati ons/pearm an_2003a.pdf > [28 Jul 2003]

P rasad, R. , 1967, A nonl i near hydrol ogi c system response model, Journal Hydraul Di v. ,

ASXE, Jul 201-221

Robinson, J. , Sivapalan, M. , 1997, T emporal scal es and hydrologi cal regi mes:

I mpli cat ions for fl ood frequency scal ing, Wat er Resources Resear ch, Vol 33 No

12, 2981-2999

Roder ick, M. , 2003, semi nar , T he pan evaporati on paradox: the global and Aust ral ian

sit uati on, Aust ral ian Nat ional Uni ver si ty.



Ross, D. , Bar tl et t, R. , Magdoff , F., Walsh, G. , 1994. F lowpat h studies in forested

wat ersheds of headwater tri butaries of Brush Brook, Vermont. Water Resour ces

Research, 30, 2611–2618.

Ruprecht , J. , Schof i el d, N. 1989, A nalysi s of streamf l ow generati on fol lowi ng

def orest at ion i n south-west W est ern Austral i a, Journal of Hydrol ogy, 105, 1 – 17.

S yeda, K., Goodri ch, D., Myer s, D. , Sor ooshi an, S., 2003, Spati al charact erist ics of

t hunderstorm rainfal l fi elds and thei r relat ions to runoff , Journal of Hydr ol ogy, vol

271, issues 1-4, 1- 21

T essi er , Y., Lovejoy, S. and Scher tzer, D., 1993, Uni versal mul ti fract al s: Theory and

observat ion for rai n and cl ouds, Journal of Appl ied Met eor ol ogy, 32, 223-250

Wat er and Ri ver s Com mi ssi on (2003) , W at er and Ri vers Commi ssi on, [ onli ne] ,

Avail abl e fr om <htt p:/ /www. wr c. wa. gov.au> [ 12 Oct 2003]

Wat er Corpor ati on. (2003) , W at er Corporati on, [onli ne] , Wat er Corpor ati on, Avail abl e

f rom: <htt p: www.wat ercor por at ion.com . au> [12 Oct 2003]

Wat er Corpor ati on 2003b, A State Water Strat egy for West ern Aust rali a: summary

document , Wat er Corpor ati on, Per th.

Wil son, E. 1990, Hydrology for E ngineers, McMi ll an, Basi ngst oke.

Wit tenburg, H. 1999, Basefl ow recessi on and recharge as nonl i near st or age processes,

Hydrological Pr ocesses, 13, 715- 726.

Wri ght, P. B. 1974, Tem por al var i at ions in seasonal rainf al l in sout h-west er n Austr al i a,

Monthly Weat her Revi ew, 102, 233-243.

C ha pter 8: Ap pe nd ic e s


8 A ppendic es 8.1 A ppendix A

T hi s is the mat lab code used to convert dai l y st r eamf low dat a int o annual str eam fl ow

dat a. Ef fect i vely the sam e code was used for all the st r eamf l ow stat ions, wit h minor

m odif icati ons due t o t he di ff er i ng peri ods of recor d.

P = xlsr ead( '612002_data');

f low = P(: ,4) ;

l = l ength(f l ow);

excel Dat es = P( :, 1) ;

m at labDates = dat enum( '30-Dec-1899') + excel Dates;

act ual_Dat e = dat est r( mat labDat es, 2) ;

t = l inspace( 0, l, l) ;

f or i = 1: l

nam e = actual _Date(i , :) ;

i f nam e( 7:8) == '70'

year _70(i ) = f low(i );

elseif name(7:8) == '71'






















el sei f nam e( 7: 8) == '00'






end

end

year_70 = year_70(f i nd(~i snan(year _70)) ); year _71 =year_71( fi nd( ~i snan( year _71)) );
















year_02 = year_02(f i nd(~i snan(year _02)) );



% now calcul ate t he total f lows for each year. .. .

t ot al _f l ow = [sum (year _70); sum ( year _71); sum( year_72); sum ( year _73); sum (year _74) ; sum( year_75) ; sum (year _76) ; sum (year _77) ; sum ( year _78); sum (year _79) ; sum (year _80); sum ( year _81); sum( year_82); sum( year_83) ;sum (year _84) ; sum (year _85); sum ( year _86); sum( year_87); sum( year_88) ;sum (year _89) ; sum (year _90); sum ( year _91); sum( year_92); sum( year_93) ;sum (year _94) ; sum (year _95); sum ( year _96); sum( year_97); sum( year_98) ;sum (year _99) ; sum (year _00); sum ( year _01); sum( year_02)] ;

year_612002 = [ total _f low'] ;

save( 'c: \m y docum ent s\ cecil ia st uf f\ t hesi s\ dat a\ cor relat ions\ 612\ year_612002. m', 'year _612002', '- AS CII ')

t ot al _f l ow = total_f low/ 1000000;

r ai n = load( 'year _9628.m ');

t _r ai n = [ 1950: 2002] ;

t 2 = li nspace(1970, 2002, 33) ;

f igur e( 2)

subpl ot ( 2, 1, 1); plot (t _r ain, rai n)

ylabel( 'Rainf al l [m m ]')

t it le('Annual Rai nf all ( 9628) ')

subpl ot ( 2, 1, 2); plot (t 2, t ot al _f l ow)

axi s( [1960 2003 0 400] )

t it le('Annual str eam fl ow (612152)')

xlabel( 'Ti me [years] ')

ylabel( 'St reamf low [GL ]')

T he f ol l owing i s the program for conver ti ng dail y r ai nf all t o annual r ai nfall .

% col 1- stn num; 2 - year ; 3 - m ont h; 4 name and? 5 - mean;

% 6- max; 7-m in; 8- total ;9-num days

A = xlsr ead( '9628_data') ;

year = A(: ,2) ;



nam e_month = A( :, 3) ;

m onthly_total = A(: , 8) ;

m onthly_max = A(: ,6) ;

num _days = A( :, 9) ;

count er =0;

f or i = 1: lengt h( year) -11

i f nam e_m onth(i : i+11) == [ 1: 12] '

count er = counter +1;

rain_matr ix = A( i :i +11, 10: 40) ;

rain_matr ix = rai n_matr i x';

rain_matr ix = rai n_matr i x( ~i snan( rai n_mat ri x) );

rain_matr ix = nonzeros( r ai n_m at ri x) ;

num_r ai n_days( i) = lengt h( rai n_matr i x) ;

maxi m um (i ) = m ax( rain_m atr ix) ;

year _total( i ) = sum (m ont hl y_t ot al (i : i+11) );

end

end

m axim um _9628 = nonzeros( m axim um ) ;

save( 'c: \m ydocument s\ceci li a st uff \t hesis\data\r aincharact eri st i cs\m axi mum_9628. m', 'maxim um _9628', '- AS CI I ') %

pre70max = m axi mum( 1:21) ;

post70m ax = maxim um ( 22:l ength(m axi mum )) ;

num _r ai n_days = nonzer os( num_rai n_days) ;

num _r ai n_days_9628 = num _rain_days;

save( 'c: \m ydocument s\ceci li astuf f\ thesi s\ dat a\ rai ncharact eri st i cs\num _rain_days_9628.m ', 'num _rain_days_9628', '- AS CI I ') %

pre70num = num_rain_days( 1: 21);

post70num = num _r ai n_days(22: lengt h( num _r ai n_days)) ;

yearl y_t ot al = nonzeros( year_tot al );

year_9628 = [year ly_total '] ;

save( 'c: \m y docum ent s\ cecil ia st uf f\ t hesi s\ dat a\ cor relat ions\ 611\ year_9628. m','year _9628', '- AS CI I ')

t im e = linspace(1950, 2002, counter) ;



m ean_pr e70max = m ean(pre70m ax);

m ean_post70m ax = mean( post70m ax) ;

m ean_pr e70num = m ean(pre70num );

m ean_post70num = mean( post70num ) ;

m ean_pr e70tot = m ean(pre70_9628) ;

m ean_post70t ot = mean( post70_9628) ;

stan_dev_pre70 = st d(pre70_9628) ;

stan_dev_post 70 = st d( post70_9628) ;

f igur e( 1)

plot( ti m e, year ly_t otal)

t it le('yearl y t ot al , stat ion 9628')

axi s( [1950,2003,0,m ax( yearl y_tot al )] )

f igur e( 2)

plot( ti m e, m axi mum_9628)

t it le('m axim um day, st at i on 9628')

axi s( [1950,2003,0,m ax( maxim um )] )

f igur e( 3)

plot( ti m e, num_rain_days_9628)

t it le('num ber of rai n days, stat ion 9628')

axi s( [1950,2003,0,m ax( num _r ai n_days) ] )

T he fol l owing code was used to cal cul at e the model then appl y cli mat e change

predi ct i ons. The codes f or al l the di scar ded m odels have not been i ncl uded.

A = xlsr ead( '9628_data') ;

r ai n_month = A( 242: 637,8) ;

L = l ength(r ain_m ont h) ;

r ai n_month_av = m ean(r ai n_m onth) ;

% get yearl y rai nf al l s

f or i = 1: L

i f rem (i +11, 12) == 0



rain_year (i ) = sum( rain_mont h(i :i +11)) ;

end

end

r ai n_year = nonzeros(r ai n_year) ;

r ai n_year_av = mean( rain_year );

% need t o separate i nto seasonal values - wi nter and sum m er ,

% summ er = jan-m ay

% wi nt er = jun-sep

f or i = 1: 12: L

wint er _r ain(i ) = sum ( rain_m ont h( i +5:i +8) );

sum m er _r ain(i ) = sum ( rain_m ont h( i :i +4)) ;

end

winter_r ai n = nonzer os(wi nt er _r ain);

m w = mean( wi nter_rai n) ; cl ear winter _r ai n_m onths

sum mer_r ai n = nonzer os(summ er _r ain);

m s = mean( sum mer_rai n) ; cl ear sum mer_r ai n_m onths

% l oad sf dat a

sf_year = load( 'year _612002.m ') ;

sf_year = sf _year /1000000; % change to GL f r om kL

% seasonal ly modi fy the rai n dat a

m od_r ai n_year = wint er _r ain + ( sum mer _r ai n - m s );

r 1 = cor rcoef (r ai n_year, sf _year )

clear x

x = m od_rain_year ;

P _m od = polyf it (x', sf _year , 2) ;

sf_mod = x.^2.*P_mod(1) + x.*P_m od(2) + P _m od( 3) ;

r _quad_m od = corr coef( sf _mod, sf _year )

% sort f or pl ott ing pur poses

m od1 = sor t( x);

m od2 = sort ( sf _m od) ;



f igur e( 5)

plot( mod1, m od2,'r- ', x, sf _year , 'bx')

xlabel( 'Rainf al l, [ m m] ')

ylabel( 'St reamf low, [GL] ')

l egend( 'Si mul at ed st ream f low', 'Raw str eamf l ow data')

% NOW WE I NT RODUCE CLI MAT E CHANGE

winter_r ai n_cc = 0. 54*mod_r ai n_year;

R = wint er _r ain_cc;

sf_cc = R. ̂ 2. *P _smod(1) + R.*P_smod( 2) + P_smod( 3);

m ean_post70 = m ean( sf_year( 3: 33) )

m ean_cc = mean( sf _cc)

m _change = ( post70sf -post _cc) /post 70sf*100

T hi s is the mat lab code used to model the 50% decli ne experi enced by the infl ows to

t he maj or surf ace wat er sour ces of Per th.

% 1st get dat a, we have i nfl ow i nto pert h dam s and r ai nf all i n ser penti ne

% col s 1, 2, 3, 4, 5 - -> year , ann, sum , wi n r ai nfall , ann sf

% r ows, 2 = 1911, 65=1974

A = xlsr ead( 'pert h_dam s_9039. xl s') ;

r ai n = A(: ,2) ; pr e_r = rain(2: 64);

win = A( :, 4) ; pre_w = win(2:64);

sum = A( :, 3) ; pre_s = sum (2:64); m s = m ean(pre_s) ;

sf_year = A( : ,5); pr e_sf = sf _year ( 2: 64);

post_sf = sf _year (65:92) ;

r _pre = corr coef( pr e_r , pre_sf)

% seasonal adj ustm ent : wi nter rai nf al l + sum m er - mean(summ er )

m od_r ai n = pr e_w + (pr e_s - m s) ;

r _season_pre = corr coef( m od_r ai n, pr e_sf)



x = m od_rain;

P _m od = polyf it (x, pre_sf , 2) ;

sf_mod = x.^2.*P_mod(1) + x.*P_m od(2) + P _m od( 3) ;

r _quad_m od = corr coef( sf _mod, pr e_sf )

% NOW WE I NT RODUCE CLI MAT E CHANGE

% looking at ser pent i ne aver ages, have decli ne of 20%

% post 70 m eans

R = 0.8*mod_r ai n;

sf_cc = R. ̂ 2. *P _m od( 1) + R. *P _m od( 2) + P_mod(3);

m _pre = mean( pr e_sf )

m _cc = mean( sf_cc)

m _change = ( m ean( pr e_sf) - m_cc)/ m ean( pre_sf) *100



8.2 A ppendix B

T he f ol l owing are the land use maps for each of the r iver basins used in this st udy. T he

l egend is com mon to al l of the land use maps displayed. (sour ce Aust rali an Natur al

Resources At l as)

Fig ure 8 -1 : Lan d u ses f or t he Denma rk C oa s t Ba s in



Fig ure 8 -2 : Lan d u ses in th e Fra nk lan d Riv er Ba sin

Fig ure 8 -3 : Lan d u ses in th e S ha nn o n River Bas in



Fig ure 8 -4 : Lan d u ses f or t he Wa rren River Bas in

Fig ure 8 -5 : Lan d u ses f or t he Bu ss elt on C o as t Bas in



Fig ure 8 -6 : Lan d u ses in th e C ollie R iv er Ba sin

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