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This thesis deals tvith 16 va ter sheds in northntstern New Bruiisivick raiiging in six from 3.000 ha to 70,000 lia that have been monitored since. The parameters analyzed n.ere: turbidity. sedinient load. TOC. EC. pH. hardness. SO,. NO;, total P. total N. TDS. Na. K. Ca. Mg. Cl. and NI-14. An automated database application \vas used to summarize the data by tvay of queries. tables. and graphs. Usine GIS. biophysical natershed cliaracteristics \vers de\.eloped for each xatershed: total area. area han~ested.road length and density. stream crossings and density. stream length and density. % area clearcut. 94 area planted. soft\vood vs. harduood. substrate. drainage. and elevation. Potential relationships betivesn forest operntions and stream nater qunlity ivcrre esriniincd 1-ia correlaiion anal!.sis. factor anal!,sis. and regression anal!.sis. Xo direct statisticall'. significant relationships nrre foiind bet\veen: ri) '.'O ma of forsst operations and other land-uses (up to 41.8% of the \vatershed) to n.ater qualit!-: and b) density of smam crossings (up to 0.873 crossings:km') to iiater qualit!.. hlulti~wiarestatistics indicated that:
Turbidity increases \vith \vatershed size r Turbidity decreases \vith increasingly iveIl-drainsd \vatersheds r Phosphorus decreases \vitIl increasing sof~\voodcontent Clearcutting increases slectrical conducti\.ity Older plantations decrease electrical conducti\ity. ------Ks!. W'ords: \vater qualit',. iiatersheds, bioph!.sical characteristics. GIS. forest operations. land use. stream crossings 1 \vould like to estend my gratehl thanks to Mark Budd. for lis assistance tvirh the dritabase de\dopment: Greg Moffatt. for his help creating maps tvith GIS:
Vince Zelazny and Darrell Fotvler for helping to get the GIS data: Andiet. Boutot. for collecting the man? grab sarnples: Steve Young for pro\.iding the project. and for pro\.iding expert ad\.ice: Dr. Charles Bourque and Dr. FanRiii 1,leng for proikiing guidance and support: Duc Banli for his help \t.ith the cornputers: Lori BUT IDr adniinistratit~esupport: Dr. Paul Arp for suptmking and enabling linanciril siippcin: and tinall!.. rhe resr of the crtxjust for hein? there.
Fiiirincial support \\.as recei\ui from Graduate Rcstlrirch :\ssistsniships anci
Graduate Teachinp .Assistantships lion1 ihe Faculty of Fortstry and En~~ironn~c.~irliI
1.lrinrigemrilt. Otlier financial support crime tiom SSERC'. and NSWC-CFS-
Indiists!. gants. Data \vas rect.itwi tiom the Laborator!- Ior Forcst Soils ;ilid
En\iror-inienial Qualit!. (J. Estq.. R. 0Iit.e. and D. hlcEltvn>,).TIie Scshr-Boitatu
Forcst \\'aterslied Rtsttarch Centre pro~idedthe infrastrucrure, k..coinputers and softu.are (.4rcInfo. ArcVien.. Strttticn.. and Microsoft Ofticr). TABLE OF CONTENTS
Page
.LI BSTRACT ...... II ... ACKNOQ'LEDGMENTS ...... III ... LIST OF T.ABLES...... uii
LIST OF FIGLIRES ...... s
CHXPTER 1 INTRODUCTION AND BACKGROL'ND ...... 1
1.1 Itirsoduction ...... 1
1.2Ratioi.ialc...... 4
1.2 CriseStudy ...... 6
1.4 CiitcI1111t.i.it Cliaractcrization ...... '1
1.5 li'atsr Qualit! Sampling ...... IO
CI.1.-\l'TER 7 \I'.ATER QL:.ALIT)' P.ARXX.1ETERS ...... 12
2.1 Inrsoductioii ...... I- 1
7.3 Turbidit! ...... 1-7
2.3 Sediinent Lorid ...... 14
2.4 Total Organic Carbon ...... 15
2.5 Elcctrical Conductivity ...... 16
2.6 Total Dissoli.ed Solids...... 18
2.7 pH ...... 18
2.8 Sodium ...... 20
2.9 Potassium ...... 20 2.10 Calcium ......
2.1 1 Ma=~neslum ......
2.13 Cliloride ......
2.13 Hardness ......
3.14Xitrate......
7.15 An~monium......
2.16Sulfate......
2.1 7 Total Xitrogsn ......
2.18 Tut31 I>liosphorus......
7.19 Otlier Pxunsters ......
CI-L-IPTER 3 Mc\PPING ......
3.1 U'ritcrshed Locarion and Dcl indon......
3.2 U'aterslied Land-Us c. Parrimeter Del.eloprnent......
3.2 W'ritershtid GIS Anrilysis and Description ......
3.3.1 Belle Ksdgwick ......
3.3.2 CanlpbelI ......
? 7 j. J.J Clsaruater ......
3.5.4 Falls Brook ......
-, 1 3.2.5 Gounamitz ......
3.3.6 Green River Bridge ......
11 _i .3.7 Gulqiiac ......
3.3.8 Little Tobique......
3.3.9 MacDouga11...... J.J.1 T 10 Odell...... 43
7 i 3.5.1 1 Quisibis...... ,
3.3.11 South Brancli Kedpvick...... ,
- 7 JJ. 13 Sissoii...... , ......
J.J.~~T~~cY- 7 ...... - 3.3.1 5 Tivo Brooks...... , ...... , ......
1 2.3.16? Cl'apske......
CI-1.-\PTER 4 D.AT.AB.ASE DEVELOPhIENT...... , ......
4.1 Introduction......
4.2 Introduction io 11s Access......
4.3 \\'orking iiiili thc River 11onitaring N~t\\orkDatabase......
4.3.1 Data Input......
4.3.: Viriving Reports......
4.3 2.1 Standard Monthl!. Report......
4.3.2.2 Vic\v Tabulx Report...... , . . , .
4.3.2.3 Vie\\ Graphical Report......
4.3.2.4 Vieiv Graphical Report by Region......
4.3.2.5 Safe Lei.els...... , ......
4.3.3 Calculate Statistics......
4.3.4 Additional Functions......
CHXPTER 5 CORRELATION ANALYSIS...... 84
5.1 Introduction...... 84
5.2h4ethods ...... 84 5.3 Results and Discussion ...... 83
5.4 Conclusions...... 89
CHAPTER 6 PRINCIPAL COMPONEYT FACTOR ANALYSIS ...... 91
6.1 Introduction ...... 91
6.2 Methods ...... 91
6.2 Results and Discussion ...... 99
6.4 Conclusion ...... 101
CHXPTER 7 MULTIPLE LINEAR REGRESSION ANALYSIS ...... 10;
7.1 Introduction ...... 103
7.2 Xlcthods ...... 103
7.2 Resiilts aiid Discussion ...... 1 OS
7.4 Conclusion ...... 114
CH.-\ P'1'L:R S CiROt'P IXIPACT ANALYSIS ...... 115
S . 1 Iiitrodiicrion...... Il5
S.2 b1ctIiods ...... Il8
S .3 Discussion ...... 170
8.4 Conclusion ...... 171
CHAPTER 9 SLlMMARY AXD CONCLUSIONS ...... 1--7 1
9.1 Original Contributions ...... 1--77
9.2 Analysis Conclusions ...... 123
9.2.1 Correlation ...... 123
9.2.2 Factor Anal).sis...... 124
9.2.3 Multiple Lincar Regression ...... 125
vii 92.4 Impact Analysis ...... 127
9.3 Recommendarions ...... 128
LITERATURECITED...... 132
APPEKDIX 1 ...... 136
Summary list of variables for correlation anril!*sis...... 85
Correlation matris for iniportant land-use cl~aracteristicsand \vater qualir!.
paranieters ...... 86
GIS and \vater quality parrimeters ustid in PCA Factor .r\nal>.sisafier bcing
broken down into themes...... 97
h:ater qualit!, and land-use pararnetcr theines dt.\dopcd for dinlcnsion
rt.duction r'sercise in PCA t'actor anal>sis...... 93
Jlain Iictor table hrPC.4 rmilts for 16 ir.atersheds in Nt.\\.Bruiis\\ick. ... 94
Siinitiiai!. o t' ai'erage u,atcr qualit!. paranicters korn 'vars 1995 ICI 1908.
incliisi\.e liv the montlis oEhlay to Nmwtiber...... '18
Suniniar! ot'land-lise paranicters iiscd in factor rinalysis for 16 \\.atcrsliccls
located in Kt.\\,Brui~swick...... IO0
Y,liiltiple linex regression eqiintions [or selected {vater q~ialityparanieters 13r sistt.cn \vaterslicds in north-nestern Xe\\. Brunsu.ick...... IO4
Ltiiltiplt. liiierir rcgrcssion siatistics for sclcctcd qrcssion cqiiatioiis liir sistc.cn
\i.ritt.rslicds in north-ircstern Nt.\\. Bruiisu-ick...... IO5
\\'ater quality paranicters and predictrid effecls ot'increnscd land-lise...... l 15
Sin~plelinear regression \.dues for h>.pothesistcsting n.ith total land-use \.ersus
17 \\.ater qualit! paranleters...... 1 19
Simple linear regression values for hypothesis testing n3li Stream crossing densir!(+ of crossingsikrn2) versus 17 \vater qualit!. parameters...... II9 LIST OF FIGURES
Figurc Pagc
1.1 Key map for sisteen ivatrrsheds in Ne\v Brunsiiick shoiving Region 1 aiid
Rcsion2 ...... -7
1 .2-:1 .An ssanipls of a folirrh order strcan~...... 3
1.3-B .An esarnpk ot'a sisth order strsam ...... 3
3.1 I.'ir'\r.dowstsenm ti.oni the monitoring station site on the Belle Lèdg\\.icC;
Rii.ts...... 34
3.2 Tti-Onirips showin2 tlie Belle Kedguick wrmhtd...... -74
7 -a 7- J.J Picrure shon'ing the monitoring station site on the Can~phtliRiiw ...... -72
T - 3.4 Two niaps sliri\ving tlic Campbell ii.atci*slied...... ->?
1 - -7.. Pictrire sIiou.ing the monitoring station site on the C1siiruntt.r Brook ...... 36
3.6 T~vonirips sholving the Clenrivater Brook ii.att.rslied...... 36
3.7 Pictiir c. slioiving [lie iiionitoriii~staiion site on Falls Brook ...... 37
2.8 T\i.o maps shoiting tlic Falls Brook tvatershsd ...... >7
3.9 Pictiire shoii.iiig the monitoring station site on rhs Gounamitz Rilu...... 3S
3.10 Tii.0 niaps shoivi~igthr Gounamitz Ri\w ivattrshsd ...... 38
3 .I I Picturr sho\ving the monitoring station site on the Green River ...... 39
3 1 T\vo maps stio\ving the Green River watershed ...... 39
3.13 Picture shm.ing the monitoring station site on the Gulquac River* ...... 40
3.14 Ti~oniaps shoning the Gulquac River watershed ...... JO
3.1 5 Picture shouing the monitoring station site on the Little Tobique Riiw.... 41 3.16 Tkvo maps showhg the Little Tobique River \vatershed ...... 4 1
3.1 7 Picture showing the monitoring station site on the MacDougall River ...... 42
3.18 T\vo maps sholving the h.IacDougall Ri\.er \vatershed ...... 42
3.19 Picture showing the monitoring station site on the Odell Ri\.er...... 43
3.30 T\vo maps shoiving the Odell River \vaterslied ...... 43
3 Picture shmving the monitoring station site on the Quisibis Ri\w...... 44
3.32 T~vornaps showing the Quisibis River \vatershed ...... -44
3.32 Picture shoiving the monitoring station site on the South Branch Kedgvick
River ...... 45
3.34 Tu.0 maps shotving the South Branch t;edgn.ick n.aterslisd...... 45
3.2 Picturc slio\\ing the iuonitoring station site on the Sisson Brook ...... 46
3-26 T\\.o nlaps slio\ving the Sisson Brook watershd ...... 46
3.27 Picture sho\ving the monitoring station site on the Tracy Brook ...... 47
3.25 T\vo niaps shou-in3 the Trac) . Brook waterslisd ...... -17
3.39 Pictiire shoiving the monitoring station site on the T\vo Brooks Ri\w...... 48
3.30 T\vo niaps sho\ving the Tu.0 Brooks Ri\w \i.aterstied...... 48
3.31 Picture shotving the monitoring station site on the b'apske Ri\.er...... 49
3.33 T\vo maps sho\iing the Wapske Ri1.e~\vatershed ...... 49
4.1 Flo~vchartof the Fraser Papers Inc . River Monitoring Net\vork Database ... 57
1.2 An illustration of an MS Access Table. depicting RiiDWater qualit!; ... 53
1.7 The diagram shotss an esample of a simple lookup Qurry nith MS Access . 3-1
3.3 The fmBuildGraph Forrn is an esample of the complesity that can be built
into an MS Accsss Form ...... 55
sii 4.5 A table that is pan of an MS Access Report that is generated ~vithuser-defined
Report criteria ...... 56
4.6 .4 sinlple MS Access Macro ...... 57
4.7 A saniple of the programming code used in a Visual Basic Module ...... 5S
4.8 The Main S\vitchboard as it appears in the RMXD database ...... 59
4.9 Data Input S\vitchboard ...... 60
4.10 The Vis\v Reports S\vitchboard ...... 6 1
4 .Il The Calculate Statistics S\vitchboard ...... 61
4.1 2 Th c. .-1dditional Functions Stvitchboard ...... 63
4.13 The indi\.idual record input Fom~...... 66
4.14 Th t. tint pagc of tlic Standard >.Ionthl! Report ...... 69
4 . 5 Choosr Table Criteria Fortn ...... 70
4.16 Choose Graph Criteria Fom...... 74
4.1 7 Safs Le\& Sn itchboard ...... 76
4.1 8 .A totals Quc'ry used to calculate the average of al1 the riiw-s in the RXISD . 77
4.19 An esan~pleof tiir standard deliarion statistic calculated b! . RRlSD...... 78
4.10 Quality Control 1 - Physical Parameters SiGtchboard ...... Sl
4.7 1 Qualit? Control 2 - Cations and pH S\vitchboard ...... SI
4.2 Quality Control 3 - Anions Suitchboard ...... S3
7.1 Fitted \.ersus dependant scatter plots for sis multiple linear regression anaiyses
for sisteen \vatersheds in north~vesternNew Brunsudi...... 106
7.2 Fitted versus dependant scatter plots for four multiple linear regression
analyses for sisteen \vatersheds in nortliwestern Ne~vBruns\vick ...... 107 8.1 Selected examples ot'stream siltation caused b) culvens. roads. and bank
erosion...... +...... +...... ,.,...ll7
siv CHAPTER 1 INTRODUCTION AND BACKGROUND
1.1 Introduction
The purpose of this tliesis is to investifate potential relationships betnwn forest land-use patterns. particularly spatial forest operations. and streani/ri\,er
\vater quality. The ovsrall focus is on esamining cumulatit.e eftécts of forest land- use at the \vatershed scale. ivith catchments ranging in area fronl 3.000 ha to
70.000 ha (Fig. 1.1). The ri~wsbeing monitored are classified as fourtli. fifth. and sisth order strearns. Esamples of founh ta sisth order streams are sho\vn in Fig.
1.2. Monitoring \{.as done for the follon.ing reasons:
to et'fectiid!. nionitor and anal!.ze cuniulati\.e \vatershed effects froni forested
lands of considrrable size at the sub-regional Ieid and still relati\.t.ly fier of
other land uses because "...large riiws are especially good indicators of
cuniulati\,e impacts." (Hrinsaker and Lr\.ine. 1993):
to keep \vater quality testing costs at a le\d tliat is sustainable (dus to budget
constraints) oiw a fiw !czar period on a n~onthlybasis. during the snoiv-fier
seaSOI1.
The specific objectik-es of this thesis are as folloi~,s:
1. to provide an o\.entieu.of a cumulative effects case.study for ivarer quality
assessment. i.e.. ivhy it is essential. and ho~vit is pursued (this Chapter):
2. ro summaiize ivhich particular {vater quality parmeters lvere monitored. and
pro\ide a rationale for doing so (Chapter 2): Fig 1.1 Key map for sixteen watersheds in New Brunswick showng Region 1 (KedgwicW Restigouche, in blue) and Region 2 (Plaster Rock. in red). Watersheds range from fourth (Sisson) ro sisth (Little Tobique) order rivers. Fig. 1.2-A. An example of a fourth order stream, this map shows the Sisson Brook watershed in western New Brunswick. Roads are marked in black. rivers in blue, and stream crossings in red. The watershed is under 3,000 ha in area.
Fig. 1.2-B. An example of a sixth order Stream, this map shows the Little Tobique watershed in western New Brunswick. Roads are marked in black, rivers in blue, and stream crossings in red. The watershed is over 70,000 ha in area. 3. to de~dopthe geograpliic contest ofcach of the 16 tva~srsliedsin ternis of the
forest covertype. land-use. soil. topography. roads. streams. and streain
crossings b roads (Chapter 3):
4. to dei.elop a Database Manageiiicnt S!s~ei.ii to t'aciliiate the esaniination of
\vater qiiality (FPI U'ater Qiiality Monitoring Database. Cliaptcr 4):
5. to nnal!.ze the connrctio~isbmveen land-use and \vater. and to test pcrtincnt
Ii!,potlirses about forest managenient operitions impacts oii strwm nater
qualit!'. b! \\.a!, ofcorrclation anal!sis (Chapttr 5). tactor mal!sis (Cliriptcr 61.
rcgrcssioil anal!sis (Cl~qxci.7). and 1i~~potIic.sistrsting ti)r ~uniulati~~ii1ipact.s
in relation to land-usc and streani crossings (Cliriptcr 8):
6. to reacli conclusions ;thout this stid!. and to prcscnt a nuniht.r ot'
recoiiiiiiendations tOr tiirrlier stitdl- (Chaptcr 9 1.
1.2 Rutiondc
Tlic trcnd in nritcr qiiality inoniiririiig in Iargt'r rilws lias hccii tti tbcus on riicrs ~vitlir.sicnsii.c rtzriciiltural ürcri. urhm land. or SC\ crc industrial point SoiIrce pollution (.lolinson CI d..1995: \lattikrilli and Ricliards. 1996: Oiiicrink ci LI/..
198 1 : Osbornc and b'ile!.. 1988: and Tufford cf id.. 1998). P«tcrltinl impacts of forest operations on streant tiater qiiality and the related nced t'or a scientific eiduation renlains a topic of public concern and intcrest. There is a need for stiidies tliat e~aluatethe impacts of forest operations on \vater quality in niid-sizrd river systenis that are predominantly forested (>80%). This interest is essentially focused on tïnding tva!.s and rneans that hdp to consene forcst lands and forest streanis as a source of hi$ quality fresh ivater. Maintenance of fresli \vater s!.sterns also assures the maintenance of:
sustainahle habitats for fiesh [vatsr biodi\wsity. including nian! sptxies such
as trout and salmon nliicli are Iiiglil!. idued by society as a nieans for
sustenance.
fresh \vater recreation.
Priiiiary concerns that relate to impacts of forest opcrations on [orcst strcams dcal
\\ith:
incrertsed strtam tiirbidit! duc to run-off froni iip-slopc soils. and dust tTom
roads:
incrertsed stream tenipesariire duc to reduccd sliriding:
incrertsed stream pollution froni nutrients and chernical conipounds
(it~~cçticides.Iierbicidt's. fitels residucs. sril ts. dust ) reltased froni ~iplands.
n,etlands. ronds. and strcani crossings: i loss of dctrital inpiits. incliiding corirsc \i.oody debris.
Man!, studics ha\.e already been donc thnt deal n.ith thcse issucs in great dciail
(Cronan et d..1999; Crosbie and Cho\\-Fraser. 1999: Hunsakrr and Levine.
19933. i-Io\\e\,er. niost of these studies are:
not done ~vithinthe cuniulati\.e effects contest of forest operations at the sub
regional scale (the focus is on using proportions of agricultural. urbm. forest.
industrial. and ivetlands as opposed to silviculture: Tufford Pr cd.. 1998):
are done in ivatersheds that coiverareas less than 500 ha: are donc for a SN ltars only:
are done ar only a fen- sites or on one river basin. thus restricting the
intcrpretation of the tindings to tir!' siie-specitic factors (Smith cl ni.. 1991 ).
Tlirrr are se\.eral reasons for thesc restrictions:
conducting an axa-\vide sl~id!. covsring nian'. \vatershcds ror man!. !cars is
csprnsii.e and rcquires considtirable commitmrnt. planning. anci ri s~simaric
and reliablc inregratioii ot'all the coiiipoiients tlirit each cicd iiiih rclinblt.
snnipling. samplr processiiig. sarnple atirilysis. and data proccssing: r near-siri~tiIrant.oiisrinril!.sis of dit: gcograpli~of niariy basins lias noi hccn
possiblc iiritil rt.ct.1~1sincc the introduction oi~1sr.r-î'ricndl!. Gtwgrapliic
[~~Ibrn~atioiiS' stcnis sotin m.
1.3 Thc Ciise Study
Thc particular srud~-areri of this thesis is locatsd in rnuch of\\.cstcri~and nonlit\mern Sc\\. Brunsnick (Fisure 1.2). Hcre. Fraser Pnpers Inc. (FPI) manages tlic forests in thrsc: \vatersheds. either as freehold or as a New Brunsnick cronm licmse. 111 total. this conipany has colIected watsr samples at the san-ipling point for sach of 16 \vatersheds silice 1995. done nionthIl. during sacli snoti-tree season.
Thesr samples nere thcn testtd in the laboratory for a 1-ariety ot'tvater qiiality parameters sucli as pH. hardncss. turbidity. sedinient load. eIectrical conductiiity. etc.. as detailed below and in Chapter 3. Doing so generated a suhstantial anioiiiit of data for tiinlier e\duation in terms of:
1. locating \vatersheds u.itli potential \vater quality problenis:
-.7 coniparing nater qualit) parameters as they change u,itli season. !.car.
geological substrate. forest t!,pe (hardu.oods \.ersus sofnvoods). i'orest
nianagrmciit pattern (?/o of \vaterslied cut. plaiited). u.atcrshed six. road
and stream density. nuniber and density of road and stream intersection
points. \i.ithin \\.atershed topograpliic \a%ons:
3. comparing \\.atcr qualit! paranieters as tlic!. \.ary Sroni basin to basin:
4. tcsting of data n.Iiether or not specitic forest nianagenient opt'rations do
have et'tkcts on \\.ater qualit!. froni 4th to 6th order strems.
an riccurate characterization of eacti nionitored \vatershed in ternis of
specilk n.att.rslled chnracteristics dcaling \virh. e.g.. c'lcvationlil
dif'trenctx catchment areri. soi1 and geological substrates. \.egt.trition
cowr. land-lise pattern. stream and road netivork strcam crossings b!.
roads. and forcst co\.er type:
an understanding of hoiv water qiiality parameters \vould bc riffscted by
thesc \vaterslied characteristics:
an understanding of how the rneasured u.ater quality paranieters compare
uitli specific nater qualit ranges for consenhg aquatic \vildlife habitats: 4. kno\\,ledge as to \vlit.tl.isr the monitorcd \vater quality paranietcrs coiiipl!.
nith esisting u.ater quality standards. whethcr these relate to industrial
\vater standards. or standards dealing \vit11 hunian healtli:
3. a database management sysrem tlnt n.ould enable an automatcd
esaniination of the data usin2:
1. qiieries about specific data in relation to \vater qualit!. thrrsliliolds. . . II. tables tliat contrast. cg.. ii,ater qiialit!, paranictcrs ncross \i~itcrsIit.ds
for sanie month. by !.exgrapiis.
iii. scatier plots rliat rc\ul dit. serisonal \ariatioiis i~t'tlic\\atcr q~iali~!
paraineters across season and t'rorii !.car io yr( 1:I1I U'atc'r Quali~!.
llonitoriny Database).
6. kilo\\ ledge as to 110~.to makc forest nimü_seitieni dccisioiis \\ liicli noiild
translate into bcst Iorilst iiianapiicr,: prnctices aiid tiiiall! a pod public
rdations project.
Using tlir databasc froni point 5 abo1.r (sec' Cliapter 1 - Datahrisc
De\.elopniilnt for more iriforniatiun). it can be sren ihat \.sr!. fe\v ot'tlic statioils esceed any of [lie inasimum allo\vablr concentrations for any of'tlie paranieters.
For esample, there are only three incidences ivhcre tiirbidity esceeds 5 NTU. This is a le\.el for driiikinp \vater: the niasimum ~urbidity for fresliivater aqiiatic life is 25 NTU. 1.4 Catchment Characterizaiion
Tlis watershed characterization involved delineating eacli n-atershed by \va! of gt'ographic in forniation s!.stems analysis (GIS). This \{.as donc b!. using ArcInlo solitvare (Enthnn~entalSysten~s Researcli Instituts. 1994). Tlie follotving land attributes nwt. obtained (set. Cliapter 3):
pcrcent of 13116 clrarcut and planted. b!, hrcst nimagenient pcriod.
forest co\.t.r 1j.p~.
stand agcs and rclated spatial distributioiis
soils.
~mds.
strcamsirit~crs.Mes.
topo~rnph! and slopc. aspect.
Sct.c.iril nictsics lia\.t' bren suggesied for his ~!.pc.of'\i~atcsslieci s~iid!
( proportioii of land-use. doiiiiiiaiice. and coritrigion: I.Iunsiikcr and Lctinc. 1005 ).
'I'his stud! uses the proportion of land-use as a priniar). independent ~xiablc
(Onierink CI (11.. 1981). It has been sho\\.n that spatial pattern can hat~a larg influence on tvater quality niodels (Hunsaker and Leihé. 1995: Jolinson cJt cd..
1995) altliougli tliere is some debate on rhis topic. Buffsr strips \siIl reduce the effrcts of spatial pattern on streams and disproportionately hi$ percentages of disturbed land (agricultural or iirban) near streams \vil1 tvorsen tvater quality problems (Omrririk cf cil.. 198 1 ). 1.5 Water Qualiîy Sampling
Tlie \vater quality sampling itself xas conducted b!. collecting riiontlily grab sarnples taken tioni the base ofSeachlvarrrshed. Sampling took place i'rom &/la! IO h'o\.eiiiber during tht. sno~v-frtxseason. Sampling \vas dont. by collecting \\atcr in
1-1 plastic bottles. racli cleaned and thoroughl!. rinsed \vitIl dcionizsd u,atrr. Tliesc bot~les\vert tlien stored in an ice-pack container. The container \vas tlien traiisported to tlic Laborator). for Forest Soils aiid Ent~ironmcntalQualit!. rit tlic
Tueecfdlilè Ceiitre for Industrial Forest Reserircli of tlic Uniiwsity ol'Scw
Brunsn.ick. Hers. tlic saniples \vers kcpt cool (4°C) prior to ritialysis. Tlicsc sanip1t.s \\utanal!.zed Ior tlic folloiving ivatcr yualiiy parameters:
tiirbidit!.
scdiiiieiit load
total cirgaiiic carbon
clcçtricnl conducri\.il>
total dissoI\.ed solids
PI 1
çod i iim
potassiiim
calciuni
ina~nesium
chloride
liardncss
nitrate arnnlonium
sulfate
total nitroçen
total phosphorus
The laboratory procedures used to determine these values are listcd in Chapter 2.
The list of pannieters anal'xd for uas deri\,sd from Kraiise ( 1997). In addition to the n~ontlil!. grab samples. hourl! Stream temperature rsadings \\.ers taksn at each nasrshed monitoring location \vith automated data-logers p1act.d in sach streani. AIso deterniined at each monitoring location \vas liourly streani disclirirgr. These data are not anaI!.zed in the thesis. but are n~rntionedhm for future rcfrrence. 2.1 Introduction
This Chapter prolkies n hiinirnni.! 01'~c~.cntcc.i1cli~niic;il md pli! sicrd \l.ater quality parameters. riII ot'\idiicli arc part ol'ihc iiioiirhl! >!i-cm und i.i\,ci.-nioiiitoring prograrn by Fraser Paptrs Iiic. l'lic \wcs qiidi~! pnrmctcss cliii 1x2 Iirol,cii cloivn into two main cnicgrirics. clicriiiç:il ;incl pli\sic~il.l'Il! sic~ilp;ii.mc'tc'i.'; rclki to turbidity. sedinieiits. clectsicd conducti\ ii!. ;ird toi;il di!;~ol\cc1 solid..;. l'lir lnrrsr is directly related to elcctriçd cniidiic~i\,ii~.~.'liciiiicd Iiiir;itiic[ci-s ictkr to :il1. siil tate. ammonium. nitrm. total ni~rop!i.IL~I;;~ ~~~~ospli~r~i~. dikhoi~cd oryiiic ~;.II~)IL sodium, cliloride. cidci~itii.pot;issiiii~i. ~ii:y~~i~i~u.atid Ii;is~l~i~ss.
The effects of tushidit!. on driiikiiig nalcr and i~c.c.i.t.aiicware pi'iiiiarily aestlietic. in that \vater \vitIl a cloudy appcirnncc Inn! Idor taste uiiappxiling
(Canadian Water Qiialit! Guiddiiics. 1 0i15 1. t.li)\ve\.cr.the tir11~1scO t' ~licwq~et~ded particles needs to be nscei-taincd hr spcci tic cases in ternis of'innrirrs i.cl;iiing to toxicity and otlier probleiiis. Lisinp Iiigli t~irhiciity\\aiCs 1'01.clcaning cis iiiduslrial prosesses may. for esaniple. led 11) uncs~~cctcdconimin;~tioii and sr;iiiiiiig. High turbidity waters can also affect :quatic liiii 13:. inierlksiiy \\ irh. c.?.. lih bclia\ior. food chain proccsscs. tàunal niici tlosal rc;irociiictian. si.spir;iiicin. and pliiii~is~~ntlirsis by phytoplankton and siibiiicrged \.cgci;itioii ( \\ cngci. 19S-ii.
2.3 Sediment Load
Sediment lorid is a nimiii-c. ~)t'lil:~;i.nldcsiispc.~icic~l p:~sticIts in \II.C;II~I{vater
(this normally cscliidcs coll«id;il p:irticlc: 1. l'lic striiiil;ird iiicilird loi. Jctci.iiiining rht. sediment load is to cvapoi.;itc (71. Ijltc'r [lit .,11.c;11ii\\ilt Wenger. 1984). Speciril tt.cIiniqiics iii;i: 112 rt.qiiircd Iiiglil! iiiinc~di/~d{{ater. For example. liygroscopic saniples \\-oiil~lrcc;iiii-c piritoiycd cli-!irig ~iiicliq~id weighing (Standard Xictliods For Tlic I:~iiiiiiii;itioti 01' \\';iicr :\,id \\';iitc\r~atcr. 1 8"' ed, 1992. pp. 3-54). Sedi~ilentparticlcs nia!. include iion-cnlloids sucli LIS silt. clay. plankton. microscopie orynisnis. or orgiiic niattsr ilirit inri!- bc lield in \\-aier by turbulence (Wenger. l9S-V). The principlc sniirce of' ~hcpilit iclcs is crozi~c. runoff into the stream. In forrst stream. sedimc!ir-laden riiiiii~'t'caiibc. indiicccl h:. hailestins if buffer strips are not intact. by routine roid ii~aintenrincc(c.g.. gradins rifgra~,el roads. \vith run-off collecting in ditches and related run-off channels). and by road construcrion in\,olving the placement of stream crossings and culverts. Like turbidity. sediments affect aquatic life. recreation. aesthetics. drinking \vater. and indiistry. Sediments also cames nutrients and chemical compounds. These chemical coinpounds may include pesticides and other tosic materials. Accuniiilation of tosic substances can occur tirst in ph!,toplankron. and froni phytoplankton io fisli. and from fish to fish-eating birds and mamnials (Wenger. 1984). Sediments aIso pose a tlireat to industrial structures and cquipmcnt because e.2.. sedimenis gadually fiIl up head ponds used for watrr storqe and slectrical poner grneration. aiid nu! plug hent eschange equipment. boilsrs. nater lines. etc. 2.4 Total Organic Carbon Total Organic Carbon (TOC) is a nisasure of the riniount of orpnnic carbon in uater. Thsrc are se~.cralsources for TOC. The nlost cornmon soiircss are huniic substances that are derivéd from partiall!. dcgradcd plant marerials (l1'engt.r. 1984). Other sources ma) bs pesticides (\\.hich are primarily organic compoiinds) thrit enter the stream through drift from aerial spra'kg. surface runoff or Iraching through pollutcd soils, or in rainfall \r.hich picks up the pesticide in the atrnosphere or on !.esstation (Wenger. 1984). TOC is analj,zed by coni.erring (osidizing) it u.itli persulfate under ultraviolet (Standard Methods For The Esaniinrition Of Water And Waste\vater. 1 s'~cd. 1992. pp. 5-1 3 - 5- 14). TOC is a very important indicator for water quality. TOC may also inipart a yellow to bron-n color. Typically. the color gets darker ivith increasing TOC. CoIor in water compromises the ar.st!ietic of tliat wmr. Large amounrs of TOC dccrcase dissolved oxygen content to such low Ic\~clstliat aquaiic lilè nia! ht. ciidaiigered (Wenger. 1984). High levels of TOC nia!. at tiiiics indicrite prcseiicc of pesticides aiid other organic compounds such as tri1ialonieth:iiic.s. and thest: ci~iildpotcntidl! pose a threat to al1 that life in the strea~iisand do\iiis~rc';imhabitats. LIS nirnticind above. Some of the coiiipounds tliat are associatc \vitIl TOC nia) hc cxcinogciiic substances (i.e.. trihalonietliaiies). Pt.sticicles aiid otlici- orgrinic conipciiiiills are easily adsorbed by humic substrtnccs. Thest. ciii~ipcid~mcr tlic strcm tlisciiigli drift from aerial spraying. surfacç ruiiot'f or Icacliiiig tliriwgli polliiicd soils. or lioiii rainfall which picks up the pcstiçide frim the ririiiosplicre or frciiii slm!eJ \cgctatii)n (Wenger. 1984). Trilialonicthancs rila! Ibrm diiriiig [lie clilorinatiori prciccss at drinking water treatmcnt plants ( \!'cngcr. 1984 ). The Canadian Water Qualit!, Gtii~lclinc.';I 1095) do no1 list a iii.i\iiiiuin alloivable concentration for TOC'. hc.c;iiisc t~ritii~ilTOC' i'i gencrrlll! not tosic. lt is. however. advisable to separatcl! rind!x i~oii-11;itui.dciiiiipiiuiids tioni [-OC. Generally. TOC Isvels that cliri~babow tlir typicril backgi-ouiid Ici-cls 01' I 11) 20 mgIl should be investigated furtlicr lhr ~.iosc;ibIrlcontcnt nt'non-nuturd coniponents. 2.5 Electrical Conductivity Electrical conductivit!~(EC) acttiaIIy nieasures the electricril resistance of water to the flow of electricril cul-reni. .AS ~~1~11.specific cciiiduct~nct:is iised as an ". . . indicator or me amouni or dis>ui\rd iiiiii ;;iïii~ iüC3 in i\.ü;i'ï. Iii g~c;:!.!!:c:c is 2 proportional relationship betivceii the aiiiniint of' ions dissol\.ed in nmx and the ability of that water to conduct electricit!,: as the anioiint ot'ions iri solirhm increases, so does the wnter's condiicti\.ir!,. Typical condusti~.ityuniis arc microsiemens per centimeter (pS/cn~).Ilnits such as niicromhos per centiiiwter (pmhojcm) are also used. Retidings are i~kcn\\itli a conciucii~.it>mt'w and are used to calcuiate Total Dissol~.edSolids (Sta11d;lrd llethods Foi. The Esainiiia~icinOf Water And Waste~vater.1 sLhrd. 1993. p;,. 2-45 - 2-46). While EC pro\.ides a con\ c'nient niid siiiiple \va> to tisccrraiil thc mwnt of ions in water. it does not pro\.idc a nicaiis of J~.rc.rniini~-.gu liicli ions iirc prcscnt. Specific analyses niust be doiic for cach ioti I! pc it'iliiit iiitiiiinaiioii is Jcsired. Hoivever. increases in EC alier loresi opa-ations \\.o~ilcisiigt-l_csr \\ hcii ii iiiight be iid\,isable to begin analyzing t'or sptxi tic icmi \\'c'ngcr. 1984 1. It is generally fourid rliat I.:C in ii~-cstsiicams dccrcr~scsu iili iiicicrising stream discliarge rate (Stanlc.!. ;iiid Arp. IOOS ). \i'otci' [li;tt ctiiiics iti coiitxt u'ith topsoil layers - such as the foresi tloor mi lhc iit~dc~~l~in: A md i3 ti~i~icrdIi?>.ers - for a short time tends to have a low EC, I'criods ot'liigli piecipitarion. licri\,y winter thaws. and heavy snowinelt events diirin~spriiig \v(iiild cause tliis. In conirrist. nater that flows through the soi1 slo\vl!. and rtxiiaiiis in contact \vit11 soi1 aiid nick nlinerals for an extended period of'tinie tends to Iia1.e IiigIi EC \.aliics. Coi~s~qliir;il!..stream EC values generally increase after iiiost of the surficial \\;?tu has bern discharged and continued Stream flow occurs due to seepagc or hase tlo~.. 2.6 Total Dissolvcd Solids Total Dissol\.ed Solids (TDS). as is the case for EC. is a nleasure of the sum amount of ions in solution in the Stream. Moreover, TDS is often calculated from EC. by way of a sinlple conversion factor (calibrated for each area). i.e.. TDS (mg/L) = EC (pS!cm) * 6.4 (Standard Xlerhods For The Esanlination Of \irater And \\'astetvater. 18"' cd. 1992. pp. 2-43 - 2-45 1. From an industrial \.ie\\.point. Iiigh values of TDS (and EC) nia! lead tu procrss interference. ix.. causing foaniinp in boilers. and enhance the rare of nietal corrosion. thertrby sliortening tlie lifetinie of eqiiiprnent coniponents and thus increasinp maintenance costs . Both EC and TDS are \wy iiscful as simple and rclati~d!. choap indicators of monitoring lvater salt content. This can bs dons nith automatic sensors and related data logging on a continuiny basis. 2.7 pH The pH of streani \vater is a direct measure of its acidity. Le., the actiiitj. of free H+ ions. Fornially. pH = - log [H-1. 'where [H+] denotes the concentration of free H+ ions in soli~tion(iii eclï). Generally. pH is ~neasurcdwith a calibiatcd pl I nieter. l'liis riicttr is xtuall!. a voltmeter, which uses two elèctrodtts. iiaiiisly consistiiig ol'a Ii- scIilc~i\cglas electrode and a reliable rrilèrence elcctrodt. to nllou tbr ri c~iitiplctecllecirical circuit between the waler. the tlectrodes, and ilic voltnwter. Staiidiird solu~iciiisi\ ith pH = 4 and pH = 7 are often iisrd to calibriitc tlic pl4 nieter (StanJaid JIctlinds 1;); the E~aniinationof' Water and M:astc.\vater. 18"' cd.. 1997. pp. 4-65 - 4-69 i. Ranges for pH in strerirns dcpci~cion: 1 ) tl_colt.igi~itlwbsiraic 01' tlic watershed: 2) substratr of the streanibcci: 7) piccipitatioii iiipiii: 4)\.t.gcration t>,pe on uplands: and vcgctation t!.pr iii tlit \( etlaiids. As I\ dl. pl l [!,pically iiicrc.asss downstream dong the strt.ani!ri~w coniinuiin~.ticological suhsrrarès arc important pH bufferinç agenis - some substratcs iscicli ris calcnrcoiis wdiiiicnts aiid niatic bedrock) have vsry high to IiigIi bulteriii~potentinls. Silicact.tius scdiiiicii~qrocks as well as light colored igneous rocks (plitc. i'h>ulitc)arc ICSS cf'fc'cti:.t' in terms of acid buffering. Atmosphcric prccipitaticiii tliat udds \vater \vilIl pH hct\\.i.cn 3 (or lower - high acid events) to 5 ivill loiver the pH of streams (increase thcir xidity). Precipitation normally has a pH of about 5.5: Forest strcanis have pH \-aliics that range from >5 to 7, rivers have pH valties froni 7 to 8. and ciceans hate pl-1 values slightly above 8. Water wiih pH 6.5 to 9.0 is rl_enerally considerd siife range for most aquatic life in streanls (Canadian \!':iter Qualit!. Guidsiiiies. 1995). Forest operations have \xriablc t.t't;.cts an strcani pH. For esaniple. clearcutting may increase pH (liicolsoti ci cd. 19x2). nia!. decrttass pH i Cisip 1982). or rnay keep pH at pre-cut let.els (Jolinsron. 1984: Patiic. IOSO: Hc\\lct~IC178: and Tiedeman. 1973). The causes for thest. \.asiations still necd tci he idcntilied. 2.8 Sodium The sodium concentration ma!. Ii;i\.t. dcirinieiitril ricstllcti: cil;'cts 011 drinking water quality. but tl.picri1 Na le\~lsprr5ciit iio scrioiis licdtli iisk for pcoplc or for aquatic life - no rnasinliim acccptahlc cimxiiiraticin tiji pciiplc or lijr :iqii:iiic life has been detïned thiis t'rir. Tlitrc is. lio\~c.\t.i-.LI iietd lili. ~iclclitii~iialc?;;iii~ill;~[ic)n particulürl u.ith road salt applic~irions;ilid tllc ~wultili; ;1c~i1iiiitl;l1ioii01' SIIand associated ions (suc11 ris CI) in soils. siilw)il5. mcl Inkes \\ iili Icin tiiriio\ -1. rates. Here. species that are salt st'nsitiw \vil1 siilfCr tirst. mtl u il1 ~_i\.c\l'a! IOiipccitls that are salt tolerant. Clearcutting or tBrcst liic.5 tciid 10 iiicsc;lsc p(is~-distiirt~;tn~~sodiunl levels but these levels arc gctici-ally cli~irc';iii:ill (Stanlc'! iind :\sp. l WSI Sodium - like othc'r nietal catiou - caii t.c anal! zcd hy \\-LI! 01' ~ironiic absorption spectronleter (Standard hlctlioils for the Esaniinatiuii ot'\Y~c.i.and Waste~viiter.18th cd.. I9Cpp. 3-1 3 - 3-1 5). 2.9 Potassium Potassium is a macronutrient (i.s.. K is rsqiiircd in major aniounis by plants) with the highest K accurnulatioiis occurriiig in plîotos!~i~tlicsiziii~plant [issues such as Leaves and green shoots. On sand~soils. K cii'tcn bcconics a groivih liniitiiig factor (Kimmins. 1987). Forest oprations nia' chan~t.potassi uni coticcntiriticms in streams by interrupting the processes of ~iptakrand iiutrieiir c!clirig (\\'ci-igcr. 1984). This often leads to in~mediareincrmcs iii strenrii K. 'I'lisivtbrr. clc.ic.rwiniii~Stream K can be a means to ascertain thc rihilit! ol'ttic uplaiid aiid iveriand \qctation to retain K during and aftsr tvatcrshcd i\.idc clisrurbnncss. hc tlicsc relatcd tti Iian.esting. fire. blow doitv. insect spidemics. etc. Çtib';t.q~iciitly.rht. rritc ot'potnssirirn losses through Stream discliargt. crin tlien be ciiiii~xii~cdwith 1iuxiI1 inputs cfiic. tci basin- widc soi1 weatliering and atniosplicric prccipit;iiion (Str111rl31dJlcrlicid'i tiir rhe Esamination of \.'riter and Nrrisic\t-rit~'i.,1 Xtli cd,. 19'17. 1y-1.3- 1.: - j-15 1. Potassium ducs nut sesrii to Ilrit ;in!. slic.ciril c. t'lccrs cm u aiei-q~il it y. Frequently analyzed for in srrcani riiiril~sis. ils prcssticc or ;ihwicc weii.s ici 1iai.c little effect on aquatic lil's. driiikins i~~t~t.qualit!. or oilicr ~LILICOLKs!.stc'1113. For esample. it lacks a separate iwntiti~iin ilic Cridii~n\\';II~'I. <)u;ilit! G~icic'lii~~s ( 1995). As such, its priiiciple iinpor-tailcc\\-ould Iic h! co~~~rilmti~igtii L:C' and TDS. and to Stream plant grotvt1-i. Likc sodirii~~;ilid ttic otticr c;irions. pcitrissitiiil is measured with an atotiiic absorption spccrrtinicrcr (Stliiidard Sdctllods tiir itw Esamination of NYatcrand Wastc\vattr. 1 8th cd.. 1997. pl1 3- 13 - 3- 15). 2,10 Calcium One of the most prevalent rnacrïinirtrisiits, çalciiini i.; geiierall! ilor Iiarmful to aquatic life. even at higli concsnti-ritioiiq. tfo\tw.cr. Iiigh cor~ccntratinnsproduce hard water. Guideljnes are tlierefore set io liniii \vater hai-diiess. pnrticitlarly L'or domestic and industrial use. hard ivarcr priiduces CaC'O; driposits in h~iilcrs.thereby limiting the usefui length of ilie eqriipmciit. As tvsll. hard ivatrr limiis tlic' effectiveness of soaps and dstergents. .4gicultural liiiiits on calciuni wnccimations in irrigation water tend to be higti (ti~ercis no rnaxinitiii-i accept;ihie cti~iccrimtion) bscause calcium aggrcgrites soils parriclcs and ~iroduccsLI iisetiil soi1 siiucrure for farmers (Canadian Wnier Quality Guiddiiics. 1905). in gt.iic.rril. cnlciuiii con~ributes to EC and TDS. Irs reli-nst: froiii soi1 ;incl kclrock minci-ds crinrrihiitcs iii ricid buffering. (Canadian U:a~t.i. @ai it!, G~iicIcli tics. i 995 ) Calcium (likt. tlic ollicr catioi~.sin his sitiri!.) is nicasiircd \\ iili xi ~irciiiiic absorption spectrommr ~Slandlircl~~lctti~cls tiii' tlw I:Y~iii;t[itm c) t' il'a~ct.~irid Wastewarer. 18th rd.. 1991. pp. -3- 13 - '-15 i. 2.1 1 Magncsium Magnesiurn is quite siiiiilar ro c';iicitiiii in tcrms til'its iinp[ic;itii~~i~tiir EC. TDS. ivater hardness. ctc. blatl_ncsiiiiii ic: dso a nincrtiiiii[ric~i~.tliciiigli plrtiirs reqiiire substantially Iess Mg than C'a. K. and S. ;\ii~ongrlic ha5c cniioii? (Sa. K. C'a. Mg). Mg is fairly mobile. and tlisrctiirc sotiic\iliar siiiiil;ir tci .Xi[, Plmis do wtscyurster Na. K. and Mg. but do ssqucstei Cri ir: uwid riiid bak. Iri wils. C'a is nwc' ~trongly bound by organic matter than Mg. uhich in t~irriis slighil! niorc bminrl tlim Na and K. In high pH soils and in hi$ pH ivmr. big and Ca rire \.CI-! similar hccause both may precipitate as carbonates cspt.ciali!. dicn isatcr is hcatzd and boilcci. Also similar to calcium. potassium and sodiiriii. niagilesii~niis nicasured iisitig :in atomic -. 3 +W. ln 5 C C 9 I= Di -.2 C-. PJ VI- 3 '2 S s E d * n- -m. CG' 3 7 -2 -3 !2- 3 rS- 3C r. < i; r! -d --.Fd 3 r. --. -2 A r.- 3' 3 G-. 2.13 Hardness Ahreferred to as salinit'.. hni-dness is directl? rcl:ited thi crtlci~iii~md magnesium in water. by [valt ~f'tlicf~lmi~la: 'tvhere Ca and Mg are nicasured iii mg![.. 2.14 Nitrate One of the ina.ior anions tlirii ~iiihc Ii~ilndiii sticiit1.i iiatcr is niirrirc. Large amounts of nitrogen in strearli irriicr niri' le;iil ro strcm ci~~icipliic~ition.i.c.. [lie production of'algal hlooms thar crin in tui-il i-cdticc rlic ~ii~iiiiinccit'liglii ml os>grrr atailable to aquatic organisri~s.Sitmtc Ic\.cl5 rire scr :II 45 tiig 1 (or l il iiig I liir NOj- W) for drinking wnter. heuuse ot'tlie risk irierIicmogl~-ihi11;111en1i~,a daiigcrous disease for infants (Wenger. 1984). Ki~satc.like chloride. is rneas~ircdiising ion chrornatography or ion capillary electropliorcsis [Standqd Methods for the Examination of Water and CIJaste\vatcr. 18th cd.. 1993. pp. 4- 1 - 4-8). Other than adding tu TDS conrcfit. nitrate is risually not a sigiliiicrint factor industrially; houever it caii be use ful for çontrol of brii1c.r-meid smbrirtlcii~ent. Nitrate almost always increases after harvesting. sspecially after hanesting hardivoods. Increases ma? be small. but increases as much as ten times the pre- hanesr control ha1.c been sesn (Symons. 1977). Anothcr potsntially significant source of nitrogen in streams is the prosimit? of agricultural lands. iihere the application of nitrogeii fertilizers and manure nlay increase the nitrogen lei.els of streams. 2.15 .Ammonium Xnimoniiin~nia' cause corrosion of coppcr and zinc alloys by fornlation of soluble nletal-ammonium conipleses. Aii~nioniun~is also a potential probleni for frestnvater aquatic life at Iiigli concentrations in the stream. panicularl! conibincd ivitli high pH leids ivhsre ammonium can be transfornied to ammonia. Probable niasiniurn safe aniounts are listrd in Table 3.1 (Canadian il'ater Qualit!. Guidelines. 1995). .-\mrnoniiin~ is measured colorimctricrill~using a Tschnicon .Autoaiialyzer (Standard Metliods for the Esamination of Watcr and M'astcivater. 18th ed.. 1992. pp. 4-84 - 4-85). Xnlmonium concentrations can also bz deterniincd uith tht. Kjeldahl distillation n~etliod.~vhere animonium is con~.ertedinto ammonia by ivay of adding NaOH solution to the sample. The ammonia gas formed is distilled bv heating the sample flask. and the gas is recovered by bubbling the distillate through a iveak acid solution, ivhsre ammonia is converted back to ammonium. The subsequent change in pH is reversed by back-titration. Mere. the amount of titrate used to retum the pH of the iveak acid buffer to its original value is directly related to the amount of ammonium contained in the sarnple. 2.16 Sulfate Sulfate is pan of total dissoli.ed ~lultiir(TDS). In gcncrril. Iii$ l'I.)'i; dues have aesthetic implications for drinkiiig \\ritu becaiisc 5 siliells. .-llsii. it is il1 advised for livestock ro driiik \vater nith \.t.ry Iiigh TIX coiiccnii-atioiis. Suifate by itself has no tasts. aiid lias little iiiipl icritioiis iii ternis ot'driiikins i\ aici-. IIo\itver. high levels of sulfate conlbirie \\.ith Ca 10 Iorin caici~iniwl thte SC;~C'.i\liicli could lead to problems in iiidustrirtl and rcsicicntial hoilcrs. 1-Iicrc rirt' no siilh~guidelines for aquatic life. As ii'ell. su1 làic le\vls arc iicm~all!iio~ Iiiui~d 3t IcwI'; Iiigh cnocigh to cause direct concerns. lndircçt t'ffects m. Ii~nci.~~.!viyiitica~it in tc!.riis ot'ricid rain. Here. high s~ilhrcle\.els crihaiicc C'ri rind 11- Icacliiiig licm soils. !Iicrcby enhancing soi1 aciditicatiuii. Like cliloridc aiid ilitrritc i1iii.r. siilliiis is ~iik.:isiircdivith ion eschange chromaiogi.uphy ai~d/orioii cripillrii~.clccti-oplioicsis < St:irid;ird blethods for the Esaniination ciI'\\';itcs ;incl \\'a~tc\\;~tci-.181Ii cd.. 1003. pp. -1-1 - 4- 8). 2.17 Total Nitrogen Total nitrogen b!. itself lias iio dirccr niasiniiini riccspt:ib'c cni~cciitrrition.It is generally recognized as a 'paranieter \$ithoiit guideliiic' tiv drinkin, \inter (Canadian Water Quality Guidelines. 1995). nieaning tliat it. b~ itscIC is cither thought not to be harnifui or not prescnt in sufticisnt qiimtities to pose ;I hcnlth risk. Nor does total nitrogen have a niasiniiim lirnit iii ille a~~iiiiticlifc u.atei.-q~inlity- guidelines. Nevenlieless. total N is a subsiaiitial contribiiior to nater siirrophication. Subsequent growth of algae and otlit'r nitropliilic vegetatioii has considci-able inlpact on the general health of the aquatic ecosystem. with iiotnblc impacts l'or spccies shifts towards nitropliilic tlora and faunri coiiiniiiiiitics. Olicri. thcx slii lis are highly undesirable in terms of general ccos! stciii Iienltli. For csniiiplc. strong ciitripliying growth leads to rapid decreases in DO. Toial ilitiogen is dcterniiiicd h! clicinicall~~ con\.erting nitrogen into amnioniuin. \\liicli iç thcii aiiiil! /cil 3s descrihd dm.e (Standard Methods hr the Esaminarioil ol' \{'fiter aiid \V~';~w\~atcr.I Xtli rd.. 1992. pp. 3-95 - 4-96). 2.18 Total Phosphorus Phosphorus concentrations iii tin~ts!rc';ini \\;iic'rb ;IIY gwcr;lll! \-CI.!.Io\v. sucli that phosphorus tciids to bc a liiiiitiiis fxtor tiir ;~qlia~icplrint grnu th c \!'t.nger. 1984). Ofien. only slight incrcnscs in pliospliorus mal. 1c;icl [o scrioii.; iiicrcxcs in plant gro~tli.Phosphariis is iiot b!. itscll' ici.!. IOY ic ti~rIiiiiiiaiis ilicrch! iiiiiking drinking water guidclincs mucli liiglisr tlim tliose le\ CI'; thlit \i.o~ildiiicrc:isc plant growh. The Canadian Writer Qualit! Guiclcliiics ( IW5 I do ncii Ilrive ri guidclinc t'or total phosphorus. No\vhere are tl-iesc Ic\.cls ahint one prt pcr tiiillioii Iioue\-er so unless increases in plant growtli are sceii it likrly \vil1 not tifti'ct nquatic lifC or other Stream processes detrirne~itall>~.Total phosplioriis is mal! zcd by acid-digestion and by measuring the resulting phosphate It.\.els uitli the Teclinicon :\utoanai!zer (Standard Methods for the Esaniination at' Writer aiid \\'risic\vater. 1 SI!\cd.. 1993. pp. 4-1 11 -4-1 17). Phosphorus is often son~eivhatnfl2cit.d b!, foi-cst opcratioils: ii niii! increase up to five limes afrer clzarc~itting(Syiioiis. 1977). lligli Ic\ds of P arc sclt.ased ~ihena forn~erlyivell-drainrd soi1 b~.coiiicsIloidcd. C'iiiiscs Lis s~ichIliiiitling L1ary. For esample. natural drain nays nial. gc~ciif ol't'b!. rad constr~iciioiis.hcavers tlood low lying areas dong streanis. fbrest liar~cstingnia' rdiice soi1 pcrnic~ihilii!-and may lead to surface ruts and subseqiisnt ~i~isticc.ponds. III tlic wil. pliiq~liorusis eenerally adsorbed by XI. and >ln ouides. Oncc I:c aici Jln osiclcs arc placed L Fc. into an anaerobic en\.isonnient. pliosplicisiis is rcleascd iiyiii. Siiliibilii! 01' Fe. AI. and hln phosphates is \:ci-> Io\\- uiidci. xiiibic ciiiiditions. 2.19 Other Parametcrs Temperature Defining strict \dues ti,r \\.atc.r iciiipcrriiiirc is Jiiliciilt iii tlic ciirl[cst of forest streams. Yorninl values th- sireaiil :ciiilicrmrc \LIS! cit'pciidiiig oi; [lie tiiw of the year. the size of' the strwii (shallo\\ or clccp. tr ide tir iiar-scw). locriiioii (elevation. latitude). proportion ot'surlricc ruri-cil'l'\crsiis \ccpiigc Ilmi. iiiiic of'day. forest cover, and e\.en tlic COIW ~)f[licstiIlstr;itc uncicrl!.ii~~~lic sist';i~li. .\]SU. defining safc lcvels for streani tmpcratiist \.as!. \\.idcl! ilcpeiiding oii tlic basis for the criteria. Many types ot'aquatic life requirc ditf6rcncc.s in strerini tcmpcrriture along the Stream (cool spots in siinililes: \\am or non-Jicyeii spots in u intu). For esample, many species ha.e diftzrent op:iiiial r;miniiini aiid iiiinin~iiniicniperatures for growth. spa~vninç.egp incubatioii. and niigratioii (\l'ciigcr. 1984). Brook trout may not tolerate temperatures esceediii? 70°C. hut rhq riiriy tolesate tciiipcratures as low as 0°C (Wenger. 1 984). Variaricm in .;trc.aiii ten1pmiLii.c arc' especicd to decrease with increasing stream ordcr tlioiigh hi2hlt.r ordci strcanis tlint iirc \vide and shallow may be espected to haw hi$ ciiiisiial teinpcrritiirc \ arialions coi1ip;ired to a well-shaded stream. In forest operations. vegtrriti~xh~iiki-s slioulrl hc. 1cf1 aroiiiid strt.ms to provide sliade for the rr.atc'r. to tiitcr wtc~runniiig it~iotlic SII-C~III.and ri) ddC- based substrates to the strcams (detsiii~s~.I Ian~estitig il~;~! incrri;iw s~i.t.,i;~i\\arcs temperatures if prcicautions ar~~ltidthc' lw!?Cr mas iitid possild! c'l.sc.\\ 1ic.i~as iveII are not taken. Dissolved Osygen (DO) The DO coiitent nt'ii.rircr is rcl;i~cclto /'{K.tctiipciilriirc. saliiiir!. atmospheric pressure. and tushulcnci.. :\qi~aliclifC rt.qiiirc~i[IO hr 'ciIr\ 11.il. and Ievels beloiv 4 mg![, \\.il1 Iia\,c dclctcrioi~~~'1'1;'cts fclr IIRN aqmic li1;'. \ mnge of 7 to 10 mg/L is typicril Ibr tiii.biilcnt Iiirc~tsrrtnnis. 1.ici~c;ic;cciiiirhLilt.ncc \\ il1 iiicrease the DO content by niisiiig the SII.~~I:I\t;ilc's u ith ai:.: ii~c'rc';l';r'dI'OC \\ il1 .Ic'c'reas~ the DO concentration bccausc tlit iwpi~tnsdiat c»n~iit;icoryiiic niiitic'r iitilizs: oxygen in the process. ( Werigcr. rFiLI/.. 1 984 1 Biological Parameters There are many biological parar-i~cterstliat crin bc iised to deterriiiiie rvater quality. These paranleters caii bc hsokci~cloivii into di fftrcnt groiips - plankton. periphyton. macroinvertebrates. Iisli. and pathogenic niicrriorgaiiisiiis ( \\'ciiger. er al.. 1984). Some species of aqiiatic insccts. lih and plaiits rirc inorc or Icss iolcrant to different components of streani cliciiiistr~.By n~easiiringtilt. presencc. und abundance of these less tolerant S~L'C~L'S.;I bio~ic portsa! ;il 01' flic qii;iIi~! of [lie Stream can be achie\'ed+A coninion csaniplt: iil'iliis arc. sirems clrriiniiig Iicalthy (or at least recentl), undisturbed) \\.aterslicds ~IXII haw a Iiirgc ariei! ol'spccics in srnall amounts as opposed io 3 retend! disiurhccl strc;~incimt;lii~ii~g ü stiidl \ aric'ty of species in large amouim. Stoiic.llics. rii;il llics. riricl cdclisllics iii [licir I;in d stases arc ofien usrd as sc~isiti\.t'indicatcirs ol'u:llcr +1;11it!. CHAPTER 3 MAPPNG 3.1 Wtitershed Location and Delincation. The 16 natersheds used in this study tiwe located by Fraser Papers Inc. to pro\.ide the most representatiire coverage of lands mnnaged bl. them (Fig. 1.1 ). Because uatersheds do not respect o\vnersIiip boundaries. lio\isver. portions ofeach \vatsrshed nlay occup! Forested lands nlanag-d by otlier forest conipanies. These arras do not represent a significanr portion of the land bcir?g studisd in nny e1.m and do not affect the anal>.sisof land-use on \vater qtiality. Thsy could affect Fraser Papsrs Inc. ability to cliangc the land-use patterns in tlis ivatcrshcds by liiiiitiiig thc arc3 ~ht'!. ha1.e ri\.riilahlc in \$.hich to concli~c'toperations. Once located. ~vatsrsl-iedswre ddintiatsd ~isiiiga combination ot'Iiardcopy and coniputsrized nlaps (in a GIS). The actual location of ihe nionitoring stations !\.as dt.tsrn1iiic.d using coordinates. These locations \vers transkrred to hürdcopy iiirips. Lsinp srrcanis ritid riixrs as ri guide. wtcrshtd boundrirics \\.rire genrrnl l y idwitit'd on the Iiardcop~maps. T1it.w \vert ustd to ascertain \vIiich GIS niriptilcis. a\*ailablrhoni the Se\\. Br~ins~vickDcpartment OF Narural Resources and Enerpy. snconlpassed the \vatersIitds. These iiiriptiles containcd inforn-tation on streaiiis. riiws. 1akc.s and ponds. stvainps. roads. forrst co\.er and mensurational data. non-forest land (agricultriral. industrial. or residential). and digitaI elevation. The digital ele\.ation data \vas con~~ertedto contours tising a function in the GIS. producing topopphical maps. W'atersheds uere roughly determined first nith the streams and riiws as a guide and tvsre then refined using the contour maps. Once drai~n.the boundary around each watershed \vas used to "cut out" the watersheds from the surrounding area. producing maps of each natershed. For more detailed information on esact metiiods used to create maps. refer to Appendis 1. 3.2 Watershcd Land-Use Parameter Deveioprnent A portion of this thesis nill consist of anal!.sis of stream nater qiinlity paranieters and land-use parameters to see if an' connections can be draum for Fraser Papers Inc. lands. \Vater quality parameters (chernical and ph1,sical) art: discusséd in Chapter 7:Water Qualit Parameter Assrssmrnt. A list of potential land-lise paraiileters follons: Total ara .-\rea of forest land Proponion of forest land i\rsa of non-forssi land Proportion of non-forest land Sil\~iciiltiirrilacti~it!. Arsa clearcut Proportion of sofin.ood dominated stands Total road length Road density Total stream length Stream density Yumber of streani crossings by roads Aireragesoi1 texture class A\.erage drainage These parameters ivere deri~.edusing GIS platforrns (ArcVien. and Arclnfo. primarily). 3.3 Watershed GIS Analysis and Description Each of these \vatershed sections \vil1 have: 1 ) a photograph of ssch !vater monitoring station. inal-be of the uatershed 7) a map(s). or reference to a map(s) in the appendis. shoiving iwious features 3) a table(s) listing qualities about the waterslied, cg.. area. percent cuiw-t>.pes. etc ... . 4) a brisf test description. sssentially to surnmxize and esplain the tables aixi ligures. 3.3.1 Belle Kedwick The Belle Kedgwick River watershed is the third srnailest watershed studied in Fraser Papers Inc. River Monitoring Network (Table 4.1). Even so, it is stili considerably larger than most traditional catchment areas subject to analysis by research scientists Fig. 3.1. View downstream fiom the (few would be larger than 1000-2000 ha). It monitoring station site on the Belle Kedgwick River. is also heavily forested; ths watershed has the smallest percentage of non-forested land of any watershed in the study area. Table 3.1. Descriptive statistics for the Belle Kedgwick watershed. Area{ha) Road Stream Total Forest Other Lthrkm) Den(!aha) Lthlkm) Den(km/ha) Stream Crossings 9,195 8,910 285 170 0.0184 1 03 0.01 12 3 1 Legead IWetiand Imest Softwood Hardwoad = Water EB Silviculture ûther Stream /C Road g Crossing 3.3.2 Campbell An average size watershed in the Fraser Papers Inc. River Monitoring Study, the Campbell River has the lowest percentage of forested area in al1 16 New Brunswick sites. Campbell River also has a fairly low number of strearn crossings (the fourth lowest) and has by far the lowest density Fig. 3.3. Picture showing the monitoring of strearns. These extrernes could be attributed to station site on the Campbell River. the large area occupied by lakes in this watershed. Table 3.2. Descriptive statistics for the Campbell watershed. Area(ha) Road Stream Total 1 Forest Other Lth(krn)1 Den(km/ha) Lth(km) Den(kml'ha) Stream Crossines 0.00706 48 Legend Wetlwd Hawest Softwood = Hardwood m Water W Silviculture i ûther ' Stream /C Road g Crossing Fig. 3.4 Two maps showing the Campbell watershed. Fig. A shows the watershed outline, rivers, roads and strearn crossings. Fig. E shows foresi anri non-îuresi cover types in the watershed. 3.3.3 Cleanvater The Clearwater Brook watershed is the second smallest watershed exarnined in this study. It has the second highest road density, however. The stream density in this watershed is also quite hi&. The high road and stream densities are most likely functions of the small size of this watershed, because of how the density indexes are calculated Fig. 3.5. Picture showing the monitoring station site on the Clearwater Brook. [Length(krn)/Area(ha)1. That is, the area decreases in size faster proportionately than the stream or road lengths. Table 3.3. Descriptive statistics for the Clearwater Brook watershed. Ares( ha) Road Stream Total Forest Other Lth(krn) Den(km/ha) Lth(krn1 Den(km/ha) Stream Crossings 7,812 7,492 320 206 0.0263 111 0.0 142 33 n Legend Softwood Hardwood Fig. 3.6 Two maps showing the Clearwater Brook watershed. Fig. A shows the watershed outline, rivers, roads and stream crossings. Fig. B shows forest and non-forest cover types in the watershed. 3.3.4 Falls Brook The Falls Brook watershed is a roughly average- sized one for this study, making its high road and strearn densities somewhat remarkable, in that they do not seem to be related to a small area. The percentage of forested area is quite large, though, and this could account for the arnount of roads. That Fig. 3.7. Picture showing the monitoring is, with a need to access a lot of forest areas, there station site on the Falls Brook. will have to be a lot of roads to do it. The number of stream crossings is consistent with the six of the watershed, however. Table 3.4. Descriptive statistics for the Falls Brook watershed. Ares( ha) Road Stream Total Forest Other Lth(km) Den(krn/ha) LtMkm) Den(km/ha) Stream Crossings 15,35 14,569 783 401 0.0261 7 10 0.0137 133 Legcnd 0 letland 0 Hmesi Softwood = Hardwood E Water Silviculture 0 ûther Stream /C Road Crossing Fig. 3.8 Two maps showing the Falls Brook watershed. Fig. A shows the watershed ouriine, rivers, rua& ariJ sirtari crussiiigs. Fig. 2 ai'iûxa fûrzs; aiid iiû~-fûï~~: cover types in the watershed. 3.3.5 Gounamitz Another roughly average-sized watershed, the Gounamitz River is generaily average in other respects as well. The road and Stream densities are neither overly high nor overly low, the number of Stream crossings is proportional tci the watershed Fig. 3.9. Picture showing the monitoring area, and the percentage (94.6%) of forested land station site on the Gounamitz River. is also about average. Lake Gounamitz (of this watershed is the site of another study by the Nexfor/Bowata Forest Watershed Conservation Research Centre. Table 3.5. Descriptive statistics for the Gounamitz River watershed. Arealha) 1 -Road I -Stream Total Forest Other Lth(km) DenkWha) Lth(km) 1 DenMa) Stream Crossinns 1 22602 21382 1231 580 0.0257 270 151 Legend Wetland 0 Harvest SoAwood Hardwood Water Silvicultwe Other " StFearn ,.A., Road g Crossing Fig. 3.1 O Two maps showing the Gounamitz River watershed. Fig. A shows the .-.,A---L,A ,..+1:-, L..- c,.nA* .-,+.A rCta?,- ...rnnn;,,nC R Ehntiref,-,,.PE* W~~ClJII~UVULIIIII, LI V I15, IUUUI Ul1U JUIVILA I1VJclAAA&si- A Ab. u d&rr r A-- -r- -a- non-forest cover types in the watershed. 3.3.6 Green River Bridge This watershed is notable for two reasons. The first is that the density of roads is slightly below average; although it is far fiom being the lowest. The second is the number of stream crossings, which is lower than would be expected for a watershed of this size. Although the road density is Fig. 3.1 1. Picture showing the monitoring station site on the Green River. certainly responsible for a portion of this characteristic, the number of stream crossings seems too low for that to be the only reason. Table 3.6. Descriptive statistics for the Green River watershed. Area(ha) -Road Stream Total Forest Other Lth(km)~Den(kmiha) Lthh) Denlkm/ha) Stream Crossinns 25403 33904, 1499 479 0.0188 398 0.01 17 127 Legend IWetland i Hanest Sofhvood Hardwood Water Silviculture 0 Iher '. S~eam /C Road Crossing Fig. 3.12 Two maps showing the Green River watershed. Fig. A shows the watershed outline, rivers, roads and Stream crossings. Fig. B shows forest and non-forest cover types in the watershed. 3.3.7 Gulquac in a sense, the Gulquac River watershed is almost the complete reverse of the Green River Bridge watershed. Despite being over 30% percent smaller than Green River, having lower road and stream densities, the number of stream crossings Fig. 3.13. Picture showing the monitoring station site on the Gulquac River. in this watershed is actually higher. The reasons for this are speculative, but the terrain may have dictated road-building with more bridge building. Table 3.7. Descriptive statistics for the Gulquac River watershed. Area(ha1 Road Strearn Total Forest Other Lth(km) Den(Ma) Lth(km) Denhdha) Stream Crossines 19256 17620 1637 281 0.0 146 213 0.0111 130 Legend IWetland II Hmest Softwood IHardwood Water Silviculrure [er Stream A Road Crossing Fig. 3.14 Two maps showing the Gulquac River watershed. Fig. A shows the watershed outliiie, rivers, roads and stream crossings. Fig. B shows forest and non-forest cover types in the watershed. 3.3.8 Little Tobique The Littie Tobique River is one of the larger rivers in New Brunswick, and even though the watershed studied by Fraser Papers Inc. encompasses only a part of it, it is still the largest watershed in the study area. Tius watershed is interesting for other reasons as Fig. 3.15. Picture showing the monitoring station site on the Little Tobique River. weI1: it has very low amounts of harvesting activity in the recent (20 to 30 year) past; other types of siIvicultura1 activity are low; the stream crossings density is very low; and it is chiefly hardwood. Table 3.8. Descriptive statistics for the Little Tobique River watershed. Area(ha) -Road Strearn Total Forest Other Lth(km) Den(krn/ha) .Lth(km) Den(Ma) Stream Crossinns 70618665064113 1022 0.0144 843 0.01 19 245 Legend 0 Wctland IHarvest Softwood .sD Hardwood Wafer Silviculture mer Stream /C Road Crossing Fig, 3.16 Two maps showing the Little Tobique River watershed. Fig. A shows the watershed outline, rivers, roads and stream crossings. Fig. B shows forest and non-foresi cuver iypes Li Uic w&ierdid. 3.3.9 MacDougall On the smaller end of the scale of watersheds, the MacDougall River watershed has average road and stream densities. The number of strearn crossings is a little below average. Softwood stands dominate and extensive harvesting operations have been carried out in the past Fig. 3.17. Picture showing the monitoring couple of decades. While present, silvicultural station site on the MacDougali River. activity has not been heavy, at least up until the time that the data used in this study was collected. Table 3.9. Descriptive statistics for the MacDougall River watershed. Area(ha) Road Stream Total 1 Forest1 Other Lth(km11Den(kmiha1 Lth(km1 I Den(kndha) Stream Crossings Legend C Stream A Road Crossing Fig. 3.18 Two maps showing the MacDougall River watershed. Fig. A shows the watershed outline, rivers, roads and stream crossings. Fig. B shows forest and non-forest cover types in the watershed. 3.3.10 Odell In some ways simijar to the MacDougall River watershed, the Odell River watershed is close to the same size and hm simikir characteristics (road crossings and density, strearn crossings, etc.). The Odell River watershed has had considerably less harvesting recently, and Fig. 3.19. Picture showing the monitoring station site on the Odell River. almost no silvicultural activity over the last ten or twenty years. This is probably due to the stand type of the watershed, the Odell is mainly compnsed of hardwood stands. Table 3.10. Descriptive statistics for the Odell River watershed. Areaiha) -Road Stream Total Forest Other Lth(krn) Den(km/ha) Lthlkrn) Den~~a)Stream Crossinas 15336 14079 1258 399 0.0195 165 0.0108 58 Legend 0 Wetland IHarvest Soflwood Hardwood Water Silvicuiture Other ' Stream A, Road d) Crossmg Fig. 3.20 Two maps showing the Odell River watershed. Fig. A shows the watershed outline, rivas, roads and strearn crossings. Fig. B shows forest and non-forest CÜW ïy-pcs iïl th@ nzkmhcd. 3.3.1 1 Ouisibis in most New Brunswick watersheds, it is possible to see the breakdown of sofhvood and hardwood stands by topography. Softwood stands occur in the valleys and hardwood stands occur mainiy on ridges. in Fig. 4.23B, 3.2 it can be seen that hardwood stands dominate Fig. 1. Picture showing the monitoring station site on the Quisibis River. throughout, but most of the stand interventions have been conducted in the valleys, where the softwood stands are most cornmon. Table 3.1 1. Descriptive statistics for the Quisibis watershed. Areafia) -Road Stream Total Forest Other Lth(krn) Den(km/ha) Lth(lcm) Den(kmiha) Stream Crossinns 23553 22234 1319 381 0.0 162 298 0.0 127 115 Legend 0 Wetland ( Harvest Soflwood Hardwood Water Silviculture L-l Other + Stream /C Road Crossing Fig. 3.22 Two maps showing the Quisibis River watershed. Fig. A shows the watershed outline, rivers, roads and Stream crossings. Fig. B shows forest and non-forest cover types in the watershed. 3.3.12 South Branch Kedgwick The South Branch Kedgwick River watershed has a very high density of stream crossings, particularly considering that the road density is not that high, nor is the stream density exceptionally low. From Fig. 4.24BI it cari be seen that this waterçhed bas had a Fig. 3.23. Picture showing the monitoring station site on the South Branch lot of activity in it over the last two decades. Kedgwick River. Also, a lot of this activity occurred near the streams, making road crossings a necessity. Table 3.12. Descriptive statistics For the South Branch Kedgwick River watershed. Areaiha) -Road Stream Total Forest Other Lth(krn) DeniMa) Lth(krn) Den(km/ha) Stream Crossings 26975 25860 11 15 656 0.0243 32 1 0.01 19 236 Legend [ Wetland 0 Harvest Sofhvood Hardwood Water Silviculture Other . ' Stream A Road Crossing Fig. 3.24 Two maps showing the South Branch Kedgwick River watershed. Fig. A shows the watershed outline, rivers, roads and stream crossings. Fig. B shows forest and non-forest covcr types in the watershed. dealing with road construction, this watershed Fig. 3.25. Picture showing the monitoring has the highest road density of al1 16 station site on the Sisson Brook. watersheds. For similar reasons, the density of stream crossings is also quite hi& Hardwood stands dominate. Table 3.13. Descriptive statistics for the Sisson Brook watershed. Area(ha) Road Stream Total Forest Other Lth(km) Den(krn/ha) Lth(km) Den(km/ha) Stxeam Crossinns 2619 2488 131 77.3 0.0295 36.6 0.0140 18 Legend 0 Wetland Hmest Sof?wood Hardwood = Water Silviculture mer ," Stream A Road Crossing Fig. 3.26 Two maps showing the Sisson Brook watershed. Fig. A shows the watershed outline, rivers, roads and stream crossings. Fig. B shows forest and non-forest cover types in the watershed. 3.3.14 Tracy The Tracy Brook watershed has the lowest density of strearn crossings among the watersheds. as well as the lowest road density. The reason for both of these factors could be that al1 activity in this watershed is Fig. 3.27. Picture showing the monitoring concentrated in the upland parts of the station site on the Tracy Brook. watershed (Fig. 4.298). The density of streams is lowest in these areas leading to fewer strearn crossings. Also, because the interventions are concentrated in one area, fewer roads are required for access. Table 3.14. Descriptive statistics for the Tracy Brook watershed. I Stream . Den(km/ha) Lth(kml Den(kmha1 Stream Crossin~s 0.0 103 35 1 0.0 146 60 Legend Wetland Harvest Softwood m Hardwood Water Silviculture 1mer , Stream Road Crossing Fig. 3.28 Two maps showing the Tracy Brook watershed. Fig. A shows the watershed outline, nvers, roads and stream crossings. Fig. B shows forest and non-forest cover types in the watershed. 3.3.15 Two Brooks The Two Brooks watershed, dorninated by hardwoods on the ridges, has had extensive planting in the valleys, most likely of softwood species. The southem part of the Two Brooks watershed consists mainly of sofhvood stand Fig. 3.29. Picture showing the monitoring types, and very little planting has been carried station site at Two Brooks. out in this area. This lowland softwood dominated area parallels where Two Brooks enters the Tobique River system. Table 3.15. Descriptive statistics for the Two Brooks watershed. Area(ha) Stream Total Forest Other Lth(km) Den(kmlha') Lth(km) Den(km/ha) Stream Crossings 14475 13592 883 374 0.0258 185 0.0128 115 Legend 0 Wetland 0 Harvest Softwood Hardwood Water Silviculture 3 iher ' Stream A, Road Crossing Fig. 3.30 Two maps showing the Two Brooks watershed. Fig. A shows the watershed outline, rivers, roads and Stream crossings. Fig. B shows forest and non-forest cover types in the watershed. 3.3.16 Wapske The second largest watershed studied in the Fraser Papers Inc. River Monitoring Project, the Wapske River watershed shares the lowest stream density (with the Odell River watershed). The stream crossing density is \ also quite low. Also, more than most other Fig. 3.3 1. Picture showing the monitoring station site on the Wapske River. tvatersheds, this one is segregated into two halves, eastern and westem. The eastern half consists mainly of hardwood dominated stands with moderate planting and harvesting. The westem half is predominantly sofhood, with linle harvesting or planting. Table 3.16. Descriptive statistics for the Wapske River watershed. Area(ha) -Road Stream Total Forest Other Lth( km) Denbdha) Lth(kml Den(km/ha) Stream Crossings 45298 41946 3352 669 0.0148 490 0.0108 158 Legend 0 Wetland 0 Harvest Softwood Hardwood Water Silviculture Other ''. Stream Road e Crossine Fig. 3.32 Two maps showing the Wapske River watershed. Fig. A shows the watershed outline, rivers, roads and stream crossings. Fig. B shows forest and non-forest cover types in the watershed. 1.1 Introduction Chapter 4 - Databasr De\dopmenr dcals uith the FPI U'riicr Qualit!. Monitoring Database. Confronttd nith such a largc and cornples assortrneiit of dnici. ir is dit'ticiilt to eiTectivttty sort tllrough it hrnisaningi'~il intim~aticin.l~liërct'ore. it is nectssary to 1m.t a s!,ste~ii (in tRis crise a tiatnbrist. application) thrit ~\.illarctcsnicitc man! tasks and niakc ii n~uclicasier to lind significant data points. For ssaniplc. itic Canadian \\'liter Qiialit~.Guidelines ( 199-3) publicaiion h~isrn.cr 1000 prigcs \i-ith nian? appendices. Starchiiig rhrougli it for rclc\.rint giiidditics or inti~iiiiriticinis iinic cons~iniiiiguid soinetiiiics an.ki\,ard. The book contains guidcliiics scr do\\ n h!, rhc Criiindim Ciilincil ot' Rcsourctt and Erwironnienr hlinistt.rs frrvrirtx qu~lit!' in Crinrida. \\';ltrlr qualit!. parameters arc discusst.d and inrisiiiiuiii and.'or 1ni nitiiiim Ic~~lsarc set hr ttw paranierers. Thrsr pcirametcrs art. sub-di1.idc.d b!- critcgury ( Drinkitig \\'riter. Aquatic Li k. Rtcrtcitional LViiter. .~~griculture.and I ndust~.). L.;sine 11s .4cccss. a datnbrtss applicaiion \vas Jc\.clopcd tlint sitnpli tics c'titcririg tlic data into ri compiiter. provides scvtral nieriris of \.ie\i.iiig ttic cia1a (in tribular or grapliical form). contains a list of relevant safe le\,t.ls. calcrilatris sitiiplz staristics oii the data. and qiieries out an)- information ~hatexcerds a 2ii.t.n h.el. This chapter pro~idesdescriptions of b1S Access (ho\v it lvorks and tvliat it can bt: iised for) and a deiailcd maniial rsplaining the FPI Warer ~ual&.hlonitoring Database. hla~i!' ssamples and actiial screenshots of the database application are included. The problem ii.ith large datri sets as generated for the p~irpost:of a.* ' .I'L*C-.-I+ +n I.nnn +rn,G nF-11 thp nilnlhPfq50 environmrnriii muniiui-iris is ilidi ii is uiiii~uirru ...- ...-...--.- obtained. and to deriieenieaningful. management-relevant information. A specialized database management application that tvould alleu a River Monitoring Xet\i.ork to analyze and manage data in an efficient manner. The database nunagenient program is called the FPI Ri\.er Monitoring Netivork Darabase (RMND).A flo\vcliart of the RMND is illustratcd in Fig. -1.1. Beginning with a Main S\vitchboard. four sections aIlo\v the user to ridd ne\v data. vien- ipphical or tabular reports. perî'oriii simple statistics on tlit dataset. or LISC additional tiinctions to th.the ivater qiiality dmtu q~iickl).detèniiinc poteiitial problcni areas. Thc purposc. uftliis C1iaptt.r is to (i) io csplain and docu111~nrbasic (iciicric 11s Access tiinctions reltimt to the dei~elopnientof Rh1XD: (ii) to esplain and do~iinic'nt tlic custom-built fiinctions and compoiicnts of [lie database: (iii) to pro\kic csrimplc.~ that allo\\.s tlit. user to step througli tliis darabass. All of tliis itihrniation is rirranged as Iollo\\.s: sections dealing \vitIl gcncric LIS .Acccss fiinciions ocçur as normal. running test (Sccrioii 4.7): sections dcaling \vitIl custorii-built or custom modi ticd RhISD 11s :\ccess i'unctions are italicizcd (disperscd tl~rou~houtthis Chapter): 0 sections dcaling i\,itli sptcific data for RXlND appear eitlier in Figures or in tcxt boses (dispersed throughout tiiis Chaptor). In Section 4.3. it is sho~vnhoiv Rh4KD can be used to : (i) enter raw data: ( ii) organize tliese data: (iii) provide tabular and graphical reporting niechanisnis: (if.) calculate simple statistics: and (Y) io flag data in reference to values deenied critical. 4.2 Introduction to MS Access Microsoi? Access is a relational database that allows for the storage of large datasets in one or more Tables. Various tools exist within MS Access for the analysis and manipulation of this data. These tools are Tables, Queries, Forms, Reports, Macros, Modules, and Switchboard system. Tables are tools used by MS Access to store data. Most databases use Tables that are intemal to each database, but it is possible to have a database access a Table remotely as well. Fig. 4.2 gives an example of such a table. Fig. 4.2. An illustration of an MS Access Table, depicting RMND Water quality , as obtained :dom the Laboratory for Forest Soils and Environmental Quality, Faculty of Forestry and Environmental Management, University of New Brunswick. Queries are standard functions enabling the user to search for data with a given date or location or other criteria that exceed a particular value. Fig. 4.3. The diagram shows an example of a simple lookup Query with MS Access. One could query the dataset for records which watersheds (e.g., Wapske or Gulquac) have turbidity readings greater than 5 WU'S in the month of November. for any year (this actually happened once in Wapske in 1997; there were no such occurrences fur Gulquac). Forms are design layout pages enabling the user to set up functions MS Access. For example. the RMN13 prompt that asks the user to input reference dates for dura is a I Station Name Fig. 4.4. The frmBuildGraph Form is an example of the complexity that can be built into an MS Access Form. This Form inciudes drop-down lists, mutually exclusive selection butions, cornmand buttons, and a sarnple graph for showing results. Re~ortscontain tables, graphs or figures, and text. A Report can be simple, containing only a simple table, or it can be cornplex, containing a combination of tabies, graphs, and text. Fig. 4.5. A table that is part of an MS Access Report that is generated with user- defined Report criteria. Using Report, any combination cf criteria could be selected. A Report may contain combinations of graphs, tables and text. The Standard Monthly RMND Report as shown in Fig. 4.14 (see below) contains 17 graphs, one table (which functions as the key for the graphs), and text headings. hlacros allow the user to automate functions within MS Access, thereby changing a potentially complex series of operations into a single, simple step. With MS Access, Xlacros run in the background and are visible only by how much effort is saved to perforrn operations. Erta a cmmnt n ths mkmi. Fig. 3.6. A simple MS Access Macro. This Macro has only one function, which is to open a Quey that calculates the average of the selected data. Modules are Visual Basic prograrnrning elements that are not directly connected to other parts of MS Access. Often, Modules cm be used to replace Macros. Also, many database operations, including Reports and Forms, use supplementary Visual Basic code to make them powerful, flexible, and/or user Fnendly. Forms used to view graphs and tables are good examples. In some instances, certain Modules and global variables must be shared. This cm. e.g., be done by way of Class Modules. Class Modules are separate code entities that can be used to affect specific Forms, Reports. Queries, etc. Fig. 4.7. A sample of the programming code used in a Visual Basic Module. Any task in MS Access that is not part of a pre-programrned function must be customized into a Module, using Visual Basic. Switchboards are a set of windows that allow the user to navigate through the various MS Access functions, without having to know database programming. These Switchboard windows (Fig. 4.8) have a simple, pre-determined layout that remains consistent throughout the application. As such, the Main Switchboard is actually a specialized type of Form (q.v.). Fig. 4.8. The Main Switchboard as it appears in the RMND database. This Switchboard and others like it are used to navigate through the database. 4.3 Working with the River Monitoring Network Database In this Section, we show how RMND can be used to: (i) enter raw data; ( ii) organize these data; (iii) provide tabular and graphical reporting mechanisms; (iv) calculate simple statistics; and (v) to flag data in reference to values deerned critical. On clicking on the RMND icon, RiiDopens to a windowv called the Data Input Switchboard. Al1 principal darabaseJirnctions of the application are reachedfiom rhis ivindorr: Thesejünctions are described here as follows. 1. Input Data (Fig. 4.9) - this button opens a Form thar allows the user to enter nm data inro the dalabase. Fig. 4.9. Data Input Switchboard. With only two options, it is an example of a very simple Switchboard. 2. View Reports (Fig. 4.10) - al1 of the reports that can be generated by RMND are reached \cith this butron; a short description of each oprion is listed belorv with a more complete e.~planationavailable in section 4.3.2 View Reports: Fig. 4.10. The View Reports Switchboard. Al1 the reports for the R,WD may be viewed from this window. Options available in the View Reports Switchboard: to look at a pre-generated and -formattecl Standard Monthlv Re~ort; to look at different Safe Levels used in the database for drinking water. recreation, and aquatic life; to create a custom table with whatever combination of watersheds, parameters and dates might be desired (View Tabular Report); a similar customizable setup for a graph, but one where al1 watersheds are listed (View Graphical Report); a customizable graph that displays one watershed against the region it is in over tirne Niew TheSeries Graph); the last button returns the user to the Mai.Switchboard. 3. Calculate Statistics (Fig. 4.1 1) - several simple statisticalfinctions are included; thesefunctions work on the whole database. Fig. 4.1 1. The Calculate Statistics Switchboard. 4. Additional Functions (Fig. 4.12) - A series of simple Queries, separared b~q parameter ripe, is used to list al1 qfrhe records where wutersheds have exceeded a given amount. It should be noted that this amotrnt does not necessarily represent a (rue marimum acceptable concentration (MAC) of a water qualiy parameter, it jirstjlags values rhat exceed a common average background level and is tu be used as a reference for FPI managers. Fig. 4.13. The Additional Functions Switchboard. This Switchboard lists the three categories of water quality parameters that are used in this study. 5. Close Window (Fig. 4.8) - thisfunction 'turns off the switchboard system, therebj aflowing an mperienced MS Access user to iriteract direct/! with the database. 6. Exit Microsoft Access (Fig. 4.8) - clicking this button will ait the database application and will close MS Access as well. 4.3.1 Data input Thcire are tlircs main n'ays ro iitiport data iiito hlS Acccss: Method 1: Siniply inpiit a table froni another source. be il another database. a spreadsheet. or a test I'ilc This niethod is quick and enay and can bs uscd n.hcn a nw databass is being de\.eloped. It is also used in tsisting databases tliai nced additional inionnnriun in T;iblcs. Mcthod 2: St.\.cral roi1.ç or coluiiiris ofdata froni 3nothc.r prograni cnn bc pristcd inio esistiitg Tüblcs in RhiND. A user \\.ouid use ~hisni~tliod ifdata C)~S;~IIICt>.pc' IIWJS 10 bs addsd. Esample: An organizatian has an entirel' nelv moniioring location and rt'l;ltcd data 10 add to RiçIND. If such data ivere arrnnged in the sanie format as the data iii the esisting 11s Access application. then suc11 data could just be pnsted into the table directly. Sr). !f FPI ii.i.r.hcldIO ~/r/dr/~/lrr$.unl LI spi-wrlsltre~IO ~lrtJlirfci. Qiiuli~.Durci Tnbfe IFig 4.2) irl RMXD. rhq*shoiilri prrlw ricitu ir~iorhe scrri~rjurt?iar. r.rac!i-for direct copj.iitg rid ptrsr hg. Method 3: This nietliod is about adding one datunl at a tinic. When a dataset is laryly coniplete and neu. data is being added periodically. this method ~vouldhe preî'erred - particiilarly iF ii ucre intended to fiinetion as the principal nieans of recording the data. .-\lthoiigli the data could be addcd to the Table directly. one record at a tiiiit.. it is oftm easisr to set up 3 Fnrm that automates this activity and simplifiss the task ut'mteririg the data (Fig. 4.12). Esiimplc: :'i database application has bec11 dc~~lopcdto aid ;in orgaiiizaticin trd;iiiJ repcirr the rcsults ofthcir ir.arcr qualit! aiial!4s. This orgatiizritioii is continiiall! ricicling to tlieir databrise \t-irh 3 n~oiiitoringsystem. AS the ne\v data beconles ri\.;iilablt.. ii c';ln bt: cntcred direcil! into the database. pcrliaps e\w riglit at the sotirçc uî'thc anal! sis. i I Ji.\ hi.o b 1 thotoi~~o1.1.Y i Eiioii~i~lLI. / 1 wutershed being considered. A drûp down list inclirding al1 watershed nomes is available TOC 7NOM 1- TDS - O Fig. 3. !3. The individual record input Form. This Form aliows the database user to put new records fiom recent analysis into the RMND. (ro prevent poren~ialspelling misrakes). iVw. tvarersheds added to the midv ici11 auromaticallj*be added to rhis lis[. Each watershed will have a sire nrimber associared wirh ir; this site number is displayed whe~ithe watershed is chosen - it does not need to be added separutely. The watershed S geographic region is nlso displayed. Wutersheds and their cowesponding site and region numbers can be seen in Table 4.1. The nat item on rhe Form are the month and the year. The four digir ~ear ion- musi Oc useu' {i.e., 1 rr ,, rrui 'r"7j. Ei* iiiüriili~üi.e iii&iri,ru' US id:,b~giiiiiiiig ;iiiN aser Papers Inc. ModandsWa1 Turbidily by Year ~atersbedList Sediment by Year aie Thqrr I 1O 1 11 Fig. 4.14. The first page of the Standard Monthly Report. The graphs shown are Turbidity and Sediment. Both are shoun as yearly averages for each of the stations (on the x-axis). The legend with station numbers appears to the nght of the graphs. Kote the safe levels for Turbidity (5 NTU) and Sediment ( 10 mgL) as indicated on the graphs. On each graph, rivers appear on the .r-ais and ~heparameters (cg., Titrbidi~ pH, etc ...) appear on the y-axk. Safe levels. when applicable, are with a dotted horizontal line. The values on each graph are averaged per year. 4.3.2.2 View Tabular Rmrt This reporr generates a Table (Fig. 4.5) based on i~ipirtfromthe user. When this oprio11is chosen, a Form (Fig. 4.15) opens with four list-boxes visible. List-boxes are an :tlS Access term meaning a box with a list of choices. Two of the List-boxes are accessible. and mVoand inactive (grayed-out). The user may choose any of the ri,arersheds/riverslisted that will be displayed. A ny corn bina tion ma! be chosen. and rhe control and shiJ kqs ma? be used to help select watershedr. Fig. 3.15. Choose Table Criteria Form. This Form allows the user to specify which parameters need to be displayed as Tabular Report 1 (Fig. 4.5). Notice that since the year(s) to be viewed have not yet been picked, an option to choose by month(s) is not available. Choosing a year allows the user to choose the month(s) they wish to include. -- - -. .. - p. p. ------Es;implc: .A person iising [lit RlliiD iiiiglit \\-ishto look rit Sdiniet~rand l'iirbidir>-Ibr. a11 ~t'thesutiinier nionths (Junc to ALI~SIwer thc Iast tlirte !.t';irs tbr tlirce specilic \\.atrrslic.ds - Odell. k'apske. and Gulquac. To do this. the user ~ouldniakc ilicir first sclcction (:Gulclux)in tlic list-box labeled Same. L\'hilc holding cio\\n thc IC-trl] lie!. the>.\voulcl thèn select Odcll and l\'apske. TIie nanies of'thr \\~aterslit'ds\{il1 hc Iliglilighted. The nest step \\.ould br to select the paranieters the! \visIl to set' in the Fields bos. Click and drag from Turbidity to Sediment - the' \vil1 Iiiglilight. To choose the last three Years. select 1996 tiom the box, fiold dolin the [Sliift] ksi.. and click on 1998. A11 three !.cars \\.il1 highlight ( 1996. 1997. and 1998). Repeat this process uith the Months box. selecting 6 (June). 7 (July). and 8 (Augusi). hlonths 6. 7. and 8 \vil1 highlight. Click on the button labeled Go! and the report \vil1 bs generated. IJ'lltt~the tlc1[(1is .s1~t11111~11~i~dhj*1~101?111, flw couibir~(~~iu~~o/~\~~LII~.s [u bc t/i.splt/~~~d is iiri.si~'~rdrrsetl. IN rliis ccis~~.~hc ilrra is aiw~1gcc(/iwrlw jwr jor '111 nrot~rhs.-4 preri~ii~ s~rrtipl~.of rlic glr/pll ir displ~~jvticri !lit boiiorrr ofrlw Form. Esample: A person using the RMND lvishes to view pH trends for tlie lutersheds o\w- the Iast three years. The user ~vouldchoose the option to View Grapliiçd Reports from the Visw Reports snitchboard. Tlie frnlBuildGraph Forni (q.\..) n.ould open (Fig. 4.4). Tlieir nest task \vould be to scroll do\\n tlie list of parameters until the>.roached pH. Tliey could click on it. OnIl, one paranirtrr can cliosen at a time liere. They uould nest clioose to sunimarizc. (rrike the werage of) the data by nionth by cI10~1singthat option. and thcii selcct the !mrs thq ivatittd to sce ( 1996 to 1998). Click on GO. and tlic graph \vould gcncrate. Tliere is ri cmmt to using this gaphical s!.stenl to \kiv data in reyrds to missin2 data. Because of' the inliertnt design of MS .\cccss (and most \licrosoIi products 1. i t' the user tries to yaph n record tliat doesri't esist. the grapli \vil1 mit just skip the niissing data. This is not ri problem if' the user resena their use ol' tlic dritribast: to thc ciistoni gr;iplis tlint are built in fiincrions of the RMND. lhtis. the grriphical rcpons that can bc. xccsscd frotn \\-itliiti die databasc arc not subjcct to this problen-i. If: Iioi\.c\w. tlie user tries to mate new custom graplis. blank spots in thc data \\.il1 Idto problrnis on the grapli. For esaniple. if one nionth \\.ere niissinz forni the range of data beginning in 1995 and ending in 1998. the graph \vould show ail of the point \vithout a break. and the data points ivould end one month before the time series. Caution nmt be esercised ~vhenilsin: this function. 4.3J.4 View Graphical Reaort bv Renion Because of the limitations imposed on rhe user with thejrsr graphical Report, rhere is a second graphical Report avoilable. This graph lists one parameter on the!- mis Cfor the reusons lisred in section 4.3.2.4 - r'iew Graphical Reporr), rhe date along the x-mis, and wu tersheds in the data series. This Report adds a nm*choice for rhe selecrion of wriables. Flnt Lakr Reglon 1 = O!, 1 1 rg 21 gH x F r r Fig. 4.16. Choose Graph Criteria Form. Thrs Form aIIows the user to graph any of the watershed sites in the database for a time period of their choosing, against any of the available water quality parameters. Results may be viewed as text reports, graph reports, or on-screen graphies. Fig. 4.17. Safe Levels Switchboard. This Switchboard allows the user to navigate to the three different safe level tables that may be viewed from the RMND. Each of the three options lists the parameters used by Fraser Papers Inc. for the study and their corresponding maximum (or minimum) acceptable concentration or level. For a list of these levels, refer to Table 2.1. Example: there is an aquatic habitat guideline for ammonium, but none under drinking or recreational water regulations. More guidelines exist both for different parameters not included in ths study and for livestock or indusmal applications; these are not significant concerns for any of the watersheds in question. 4.3.3 Calculate Statistics There are seven distinct statisrics that may be calculated using the RMND. An example of one of these statisrics is shown in Fig. 4.18. The sratistics are calculared bmed on choices enrered 6). the user (starting and ending dates). Fig. 4.18. A totals Query used to calculate the average of ail the rivers in the RMND. For the exarnple used above, the dates selected ran from lune to August, 1997 to 1998. Average - calculated based on the station and range of dates selected (Fig. 4-18), Count - the nurnber of measurernents for that given station and range of dates, Maximum - the highest value for that given station and range of dates. Minimum - the lowest value for that given station and range of dates, -Surn - total of al1 values in the range selected, Standard deviation - standard deviation of the average for the range selected (Fig. 4.1 Q), Variance - variance of the average for the range selected. Ir must be noted that dates need to be enrered in a speci/ic formar. Da'iuringfiom this formal mur came enws or crashes in rhe ~tfSAccess program. Examples of the format are provided in a iexrbox displayed th en rhe desired statistical test is chosen. The i-ears selected shoiild nmer tweed gomard or backwnrd in rime) the range of dares cojliained in the database. Thejrst year in rhe darabase is 1995 - no year earlier than this shdd be selecied as a criterion. ifrhe newest year hlere 1999, mering 2000 could ahcause dificulties. Finail),, the year mus; be eniered in four-digit format. LSing an abbraiarion such as '98 insrend of 1998 will cause emrs. Fig. 4.19. An example of the standard detiation statistic calculated by LMND. This statistic is for dates fiom June to August, 1997 to 1998. in conjunction with the statistical average in Fig. 4.18, ths gives the average value plus or minus a certain amount. ~l~~~1~~~,/1~01~11996 10 1998, /il!* 111c 1110111~1()/'.Ml!* 0111r ~llld0!1C (?f'lIlc.st~lliol1s dilill '1 hscLi r~~cwci/iw.Ilqi- il7 1 Y 9: I~LJa\.clvr,ye iinlllti c~rlcwicrrecicorïwi~' it.siii,y ilw 1 Y 96 irltci i 998 i.rdnt~.v. Exarnplc: b'licn a statistic is sclcçted. the uscr \\.il1 bc proniptsd to enter ri range of'datcs. Ir the iisrr nislied to see atwagcs. \vhen the .A\.eragt.s button \\.as clicked on thc Calculate Statistics s\vitcliboard (Fiy. 4.1 1 ). tliey \vould hat.e to respond to sevcriil prompts, If the user lvas interested in seeing Septeniber to November for 1997 and 1998. the! n-ould type in 1997 at the tint prompt 79 Esain~lc:The :\quatic Habitat guidelins for scdiment is 100 nigiL (Table 2.1 1. Tlic rilm nciw gt.t close to this aniount. In fact. b!. the end of 1998. tlicrc arc onl! f0ur cascs of scdimcnt esceediny e1.t.n 10 rng/L. If the real yuideline \vert. iised. no crises 01' rt.lati\.ei! hiyli sedinient concentrations \\.ould e\.er bt. tlagged. Cising a niuch lo\\cr rckrenct. \.aliie (likt. 10 nig."L).a rt'lati\.c'l\. Iiigli anmmt (36 mg/L in Falls Brook) can be found. Rscause ~his\due surmounts e\w the nest Iiiglicst \dues hy a f'rictor of thrcc (and it is grearer tlian the average sedinient valut. by betiveen 10 and 100 tinies). tliis record should bc invt.stigatt.d. 1. Quality Controll - Physical Parameters (Fig. 4.20) There are five physical water quality parameters: 4 Turbidity - 5 NTU's, Sediment - 10 mglL, Total Organic Carbon (TOC) - 10 mg/L, 4 Conductivity (EC) - 150 pS/cm, and Total Dissolved Solids (TDS) - 960 mglL. Fig. 4.20. Quality Control 1 - Physical Parameters Switchboard. Five parameters are listed under this heading. They are Turbidity, Sediment, TOC, EC, and TDS. 2. Quaîity Control2 -Cations and pH (Fig. 4.21) There are six chernical water quality pararneters in this section: Calcium - 20 mg/L, Potassium - 1 mg/L, Magnesium - 5 mg/L, Sodium - 5 mgL, Hardness - 80 mg/L, and pH - below 6.5 or above 9.0. J HU-neomdi.1 J pi (6.5 a >PO1 J RatunbAMimdFvrb* i Fig. 4.2 1, Quality Control2 - Cations and pH Switchboard. Six parameters are listed under this heading. They are calcium, potassium, magnesium, sodium, hardness, and pK 3. Quality Control3 - Anions (Fig. 4.22) There are six chemical wata quality parameters in this section: Chloride - 5 mg/L, Nitrate - 0.3 mg/L, r Ammonium - 0.1 mg/L, r Sulfate - 10 mg/L, r Total Nitrogen - 0.75 mg/L, and Total Phosphorus - 0.1 mdL. Fig. 4.22. Quality Control 3 - Anions Switchboard. There are six parameters listed under this heading. They are chloride, nitrate, ammonium,sulfate, total nitrogen, and total phosphorus. CHAPTER 5 CORRELATION AN.4I.YSIS 5.1 Introduction In this chapter. connections betkveen \vatershed characteristics and nater quality parameters are in~wigited.The panicular objecti\.e is to relate the \vatershed characteristics generatcd and listed in Chapter 3 to the \vater quality parameters in Chapter 3, b!. of correlation analysis and Pcictor analysis. In this anal !sis. the natershed-based abssn.arions refcr to: topograph! (sixof ~iatershsd.ele\~ational diflerences. slopss. asprc1 orientation). substrats t1.p~(igneous. mstaniiirpliic. sedimentar!.. organic). toscst co\w (hardu.oods. softii-oods). otlisr surface fsatuses (uetlands. lakes). corridor net\vorks (streams. roads) and intersections (streain crossings). forest hnr\.esting history (uncut. and recovering forest ~qetationbased on 5 !car afe classes). silviculture history (plantarions. based on 5-year ags classes). 5.2 Methods This annlysis involred fifi) \vatershed and \vater qualit) iariables (Table 5.1). A simple correlation matris using listwise deIetion uas made. Each value in the correlation table represents the correlation coefficient for each variable pair. Though correlations of less than r=O.j niay bc s~atis~ic;~Il>signilicani. the!. are gencrrtlly not practically significant ( Abacus Concepts. 1 996). Table 5.1 Summary list of variables fui. correlation riiirilysis. Landscape Land4 'sc N'ritér qua lit^ . Waterstied Tiirhidit: - \Li\. .\\S..Slopc Region SC~~IIICIII - \LI\. ..\ig.. Slope Total Area (ha) TOC -- L~;I\. ..\&S.. Slope Fore71 Area (ha. 4'0) ITC - 'v~J\;. A\;.. Slope Other Area (ha. %) TDS Ma\. .-\ig . Slopt: Stream Length (km) pH - Jin. !\\S.. Sk1pt Stream Density (km'ha) Y;1 -- \la\. A\:.. cilope Sof~ood(%) K - \la\. .Ab g.. Sli>pt Hardibood l%) C;t LI;)\. .\\S..hIt1pc Igneous Substrate (4'0) \lg \lil\. .\\S., Slopc Metamorphic Siibstratrt ('0) tlnrdiicss - 11,1\.:\\g.. Sh~pe Sedimentary Subsirate ("O) ci . ?.la\. .A\ ;.. Sh1pe Organic Substnte (OO) so4-s - ILI\. :IL g,. Slopc Water (46) Y( )>-Y !la\, A\ S.. Slopt Drainage - P. M. W ('a) St 14-S Mi!\. :\tg.. Slope Elrvarion - Min. Mas. DifC (rn 1 T~irnlP - LI;!\. .Ai%.. Slupc. IIX LI;i\. :\tg.. Slapr I~ciq)cr;~[~rc 5.3 Results and Discussion By lvay of correlation anal~4s.it lias Iiiiii~d:lut man! nt'ilie i\~rcrslicd characteristics are strongl!. correlated u itli saçh orhci (Tablc 5.2). For esample. watershed size is strongly positi\.cly coriclrttcd iviili totn! sirem Iengih and total road length. The difference in elevation uithin a natershed is positi\dy correlated to watershed area. Drainage is negatively correlated to ivakrshed size. .As u.atei.slied size increases. the percentage of land thrit is \\dl-drained decreascs as do\wstream areas become increasingly flat. - --? C ! --=---- := 5, tli + W. := zZï-=< 5 2 - --- TY?.---?=it -- .- .->S$?Fm..-* 4 O =%- L--y2$;.AT??>:& = - 5 zL--, ,- ->c: I<' - .* 2 2 2 2 &SL<< 3 - - Turbidity is most strongly and positii.c.ly corrclatcd to % oi'i~on-tbrcsttdarea prir watershed. For the area of this study. rnost non-forest Iünd is uetland. Iherefore. as the proportion of ivetland increases. so dos5 [lie t~irbiclit>in the ri;c'rs. Sediment values are roiighly equally corrclated to total 3~~3.forest arca. non- forest area. road length. and streanl Icngtli. Conductivity and sulfate shon soiiic corrt.i;itioii to the six ol'tlw u ;11crs11cfd:as the watershed increases in six. EC' and SO1 conct.ntratiniis inciu.sc as \ic.ll. The amount of softwood dominatcd land is corrcliircd tù EC. Leaching of ions is higher tioni sutiuood ~triiid~ilian in tirirJniiud \t;iiiJ\. tthich could lead to greater espons ot' the ions illa1 contrihiire to EC' rnr.asiircrncnrs iri the stream. Substrate t>pesappear to haie corisidcrablc irltl~iciict.on iratcr q~ialit> panmeters: Turbidity, sediment. TOC. pH. and KO1 arc' a11 corrt'lated 10 SLL~S~T;~ICt! pe. TOC has a strong negati\.e corrclation \vitIl the riniount of'n1ctrimcirphic substrate, and a strong positive corrclrition ~vitlitlic amount oforgnriic substrate. Turbidity is also negatively correlated witli itietainorphic substrates. but positively with sedimentar!. substrntes: sedinient loxi in the stream liillou~sthe smepattern as turbidity. but the corrclations are less strong. Nitrate is positively correlated ivith nietamorphic s~ibstrate.arid negntivtily with sedimentary substrates. a Wetlands have a strong effect on pH: pH is ncgativel>.correlriied io ;gnt.ous substrates (ponding ot'water). orgaiiic çuhsrrates. non-forcst nrsa iaiiim~ntof wetlands). and the aniount of' open iiarer. 5.4 Conclusions Of gcneral interest are the corrcl;irioiis hc.i\~.c.cnsrittstrittt t!,pe (iti~.tmorpliic. ignéous. sedimentary. and organic) aiid rlrain;ige clriss (poor. iiiodciritc. ml \vsll). Organic substrates. open ivatcr rireas. ;incl poor drainqc xcas dl tc'iicl to hc niore abundant when igneous siibstra[es colvr a I;iigc. pair oi'tlie nritcrshcd. ilic ~rppositr appears to be true for trietrim»rpI~içstthstraics. Igiico~i~stihstraies teilcl IO hi' ICSS fragmented and therrtorc lsss pcrmcalilc tliari iiictmorphic siibsiraics. Scdinicniary substrates and metamorphic substrates ;ippc;is io liat,e sin~ilrir1;iiidsc~ipc iissriciarions. but the correlations art. lotver for tlic I~)I.IIICT. Hardwood and soti~iaodartas rt irliiti \wrcrslicds cnrrclart. io csiciii ol'recent harvesting and planting oprraiions. H:iist estins (iiiriiiil! CIC;IITLI~~~II~)c;~rric'ii OUI from the years 1979 to 1984 is nor c~irrclacritlIO thc' ;ii~ioi~ilti~f'soli\\iiod ils Iiardwood in a given ivattirshrd. Local forest management demands rippearcd IO chriiige oiw ilie ncst ti\'e years. leading to a shift in targeting soIi\vood siands. This trend has contiiiusd since. In contrast. hardwood dorninated ~t.atcrshcds1m.e seen a decre~isein clearcutting activities. Based on an analysis of the dm. the aiiloiint of planting in al1 esaniincd watersheds was high in the early- to mid-eighties. aiid decrcnsed IO ruiighl! 20% of its peak arnounts by the nineties. Plantiiig &,asniore commoii in soSt\\~onJdoniinated watersheds than in hardwood dominatd i\-atcrslxds. p;iriicularly in tlic c'ighties. Since 1990, correlations bet\veen hardi-o«d or sutluwd plantiiig p:ittcriis rise less obvious. Because of the effect increasing ignto~isand osgnnic substratcs. \\ ritcrshed area, and arnount of open water pl-l drcrtxscs and tlic i-iwrs hcconie iiiort. ;icidic. Therefore, while pH valucs miglit currciitly twt drop hcloii critical tlircsliold \dues for aquatic life in the rivers. x~i\,itics11131 mi@t c~ntrilwtcto xidi ticatiiin (conversion to soft~voodforest by plantiii+ tus r.s:iinplc) shoiild IIC ;ii oic1c.d in watersheds with a high degrse of ivctlriiicls. CHAPTER 6 PRINCIP.4L COhIPONt3T I'I\C'T(?R ANAl.'i'SlS 6.1 Introduction Factor analpis is done to csplore tht iiiidt.rlyiiig corselaticin partci-ils aniong the various field obsenations. In particdur. Iàctor anal'sis hclps IO rt.1 c';il those variables that likely esplain most of tlit. titlci \aririiiims as cnpturcd h! ilic original dataset. In so doing. the overall data coinplcsit!~is lbrn~all!csplriincd 131 a ICw factors. Factor analysis is rilso ussd to scducc laryc iiicinbcrs o1'corrclmd \.ariablcs into small number of factors (Ahricus Cc-~iicep!';. 19%). 'l'lit outcimc is criIIcd dimension reduction. the purposc of \\liicli i5 [O rtxlucc. a Jairi';tr to ri . The tvpe of factor analysis ustd is principal coinponcni aiial!~sis il'(..\). PCA is used because it is lvell suitrd \i.licn ilic data ii; r;ii~I011iniid the s~I~cti~~n01' variables is complete (Abacus Coiicepts. 1006). PC.4 Iixi bccn iisd 1;)s rliis t!.pr of study in the Great Lakes region (Crosbic and Clilni-l.'r;iscr. ILW). 6.2 Methods The variables that were selected for bctor nnalysis \vsre taken Srom two main sources. The first source is GIS. \sliich allowed for.the devrlopmttnt of land- use païameters. The second source is the watcr qualit? arial!,sis itsel S. Laid-~iseand water quality variables are listed in Table 6.1. Table 6.1. GIS and water qualit' paranieters iisd in 1T.-\Factor Aiial!sis after averages. masiniun~s.and average annual lincai slopc. I'riisy \wiables \vue taken from these initial paran1c.iei.s. Watershed Characteristics U'mr Qualii\ Vxiibles Total Area (ha) Forest Area (ha) Non-Forest Area (ha) Road Length (km) Road Density (kmha) Stream Length (km) Stream Density (knijha) Stream Crossings Crossing Demit!. ( flkni'i Area Clearcui 1 979- 1 99h (%) Ares Planted 1979- 1996 1%) Softwood Dominated Stands (" 1,) Hardwood Doiiiinated Siands (" O 1 Substrate Type (Yi) Drainage Ciass Elevation (m) relatively independent of one another. 'l'liis sicp \us rakcn ris LI filitllcr nic';lsiIre of dimension reduction. For esainplc. hnrdiicss is calcirlrircd tioiii two ntlici highly correlated variables. calcium and i~~agnesiuni- 1ixdiit.s~ automatically reprcsents those two variables (Chapter 2.13 ). Toial dissol~.edsiilids are caIcliI;ltcci fil)in the electrical conductivity measuremcnt. Vv3tcrshc.d characteristic and \vater quality parameter values are listed in Tables 6.7 and 6.3. Table 6.2. Summary of average uater quality paranirtcrs from years 1993 to 1998. inclusive for rht. monihs of iila! io Koi,rnibrr. Warershed Turbidity Srdiment TOC EC pH 1-lnrdneis Stilfatr. Nitrnic Toral P Nanie (STU) (nig L) (mgL) (uS'cni) tnig LI ~rngLi (mg 1.1 (in: LI Belle Kedgu ick Campbell Clctanratcr Falls Brook Gtilquac Gounamirz Green Riw Lit~lriTobiqur: XlacDougall Odeil Qiiisi bis Sisson S. Br. Kcidgi ick T\w Brooks Trac!, V'apskc. Water quality parameters Lvere saiiipled h!. triking grah sainplts oiicc Fer month from May to Noveinber. oirera period of scverril years. Each \v;wrslied has one value each for forest area. road length. etc. so the water q~ialir!. dmlias to br made compatible with the land-use data. This \vas done by con\wting tlic \rater quality variables from nionthly and ycarly valiies to a\vsage \~~l~ics.masimm values. and average seasonal linear slope values. For esaiiiplt. tlir tliree \xiables for turbidity were average turbidit!.. niasiniiiiii tusbidir!.. and slope ot't~irhidit!~. Averages were calculared arithriicticrill!~: ;\II a\.ailahlc riirbidit! i;ilucs Iiir txch station tvere summed and dii,idt.d b!. thc nuniht~ot'rccords. A\-cr;igc sCawiial liricar slope was calculated by tinding the lincar slcipc tiir c~ichycx oI'iiicasui~c.r~it'~it.ti-om 1995 to 1998. This lincar slopc. duc is rlic cqiii\,alcrir cit'tlic slopc coci'ficic~irfor a simple linear regression hct\vccn tlic nuiiit"rica1 datc b'. nioiitli aiid rlic \ :.iluc t'or that month. In other words. thc moiitlis ol'hla! t« Soiwnber (ii~inicric:ill! rq~ic4;cntt.das months 5 to 1 1) are compared to tlic \.;~riahlc\.rilut. tiv tach ilioi~tli(toi. csaiiiple. 3. 4. 5. 5. 4.6. 7 NTU). One dope coetticicnt is calculatcd hr- edi'car ot'data collection. These values ivere ai~sagrdto produce a single a\.cnige ;inii~iaIlinear dope for each watershed. The three types of ivater qualit' variables nerc cli~sciifor dii'lCrciit rcasons. Average values were used to to see if' certain watcrsllcd land-use cllaracic.ristics were related to overall water quality paranieters (for esaniple.~substratct!.pe and hardness). Maximum values were incliided to ideiitify specific pcak ci.c'nts. cinabling the cornparison of land-use characteristics to tlir niost potentially linrmfiil tvater quality incidences. Average seasonal linear dope \vas includcd bcca~isctherc is thought to be a relationship bettiwn certain \tater qualit? paranittt'rs aiid rhe month in which it is sampled. In ofher ~vards.soiiie vttlucs are us!~rlll~louer in \la!. than November, while others are Iiiglier in Ma! tliaii Sovmbtr. ln orcier tu ~.c'llcc~this. slope values for each pnrarncter (positi\,t.or ncgati\c) ucrc iiicIi~Jcdin ~ht';~naIysis. The procedural steps follow: Enter al1 variables into a corielatioii matris: an! \.ririablt's highl!- ccii.relared ivith one another are easily identifieci froiii this nnril!,sis (t..g.. EC' and TDS asc highly correlated). Choose unique variables (t'.g.. 7'DS is cicri\.t.d iiiritlicniaticüll!~tTcm IIC': Hardness is an arnülgainatioii ot'C'a niid \Ig) Categorize remaining variables inrii tlisiiit.~b;isccl on intririsic cliarrir~tc.ristics (e.g.. EC. pH. and I-larc!ness arc in~sin~icrtll!~~l;ltcci IO one aiiotliei. ht111c.y are derived separately during annl!~sis). Run a PCA factor analpis on cricli tliciiic. Using PCA output. choose one \xi;iblt' to serve as ri pios! hi. ciicli tlicnit. bassd on the highest factor loadings (cg..F:C lias a Iiiglicr Iàctnr Iiiding r r' = 0.060) then either pH (r2 = 0.921) or tlardiicss (r' = 0.95 1 ): FC is tlicrctiirc !lx pros) variable. Amalgamate al1 proxy variables into one aiinl~.sisand riin a Pr:\ ag~iiri~ising just those variables, From the latest PCA. select the siyificaiit factors iioni tlic PCrl o~itput. Critically analyze the PCA output and derive conclusions reyrding Iniidscape and land-use effecrs on \vater qiiality (e.g.. PC 1 (cl.\.. Section 6.2.3 - Frictor Analysis Results) denionstrates hou lie sizc aiid pli ysiclil mriksup of a watershed afîect turbidity rendings 1. For the firsr round of'tàctor anal!.sis. the ctiniprt.lisnsit~elisi cil'\ :iririhlcs (land-use and viater quality) was di~.idediiito sel ml t1ienic.s (sicp 3 O!' tlic procedure). Each of these thsrnrs reprcscriitd li separaie caiqor! al-daia. In ordtr to constitute a theme. al1 coniponcnt twiah1c.s liad ici sl.iou' a strorig corr~l~irioiito rhe priniq factor in the PCA. Thc \wiahlc ~\itliilir. higlicsi Licror Imdiiig I i.c.. tlic strongest carrelation to the tlieiiics) \iris wlcctcd 10 rcprcscni dl ~-~iri~ihlc.siii tliat theme. The thsmes. dong ~viththc. prosi~xssc.icc~cd ir, rcprcwit iliciii liwd in Table 6.4. For exarnplc. Thcmc. 7 consisrcd iii'coricliicrii.it~.tiai~ii~css. ;iiiLf pH. Conductivity \vas chostin as iht. pros). \iiririhlc liir itiis ~lictiit.. Somc variables do not SCCIII ti) !il i~itii;III! iif'iiir! f:rlcmrs. I'crclirir;i_ic nf water. sedimentar): substrais. susyt.ndcd sc'cii~l~cnts,artas CIC~I-CUI md pI311icd.and minimum elevarion - nonc lit tasilj imc, oilicr c;ltcgorics. I'hsrc1i1r.c. ilicsc ~ariables are ahincluded in thc final rouiid ol'ficror ;ii~ril>~sis.S!;isiiniini and ;IW].;I~C nnnud linear siope valucs for \vatCr qualit'. pxmctcrs Ii;ld ICSS signi Iicm c'ii~~cl:itioilsto other variables than mean ivarer qualit! Ii;iruiictsrs. I'l.ic.3~\.alties ir crc" 1101 included in the fina1 round of the anaIlsis. The next step in dimensint~rcdiictirin is [lie riggregiioii ol'rlirl pros' variables. Once compiled in10 a siiiglc daubase. PCA is asain riscd to SCXCII for factors within the data. Factor arialysis liinits the tiiimhcr of'\.ariables tlilit nlay be considered in one anal!sis to the iiunihsr of records (in tliis case 16 uatcrsheds) minus one. Therefore, no more than 15 ~xiablesma!- be incliidrd in dit :iiialysis at cLk.0 Jall?M 1 HX.0 .loO,( O<(,'() 31r!~qicllq SfIOw PJii!l?JCI II3M 9 1 LÇ'O ~IIXII!I>~S 9VO'O VOS SOX'O- LON COh'O 3 ).l. OVON Li!p!q~~l.l. I one time. Even afier the two stages ol'dirncnsilin rcductian. tlirrc are appro\rirnately 20 variables to be considered for tilt. final .inrilysil;. 6.3 Results and Discussion Three significanr factors are clsar ti.oi.i~this anal! sis ri1it.r ~cstiiigriim! variabIe combinations (Table 6.5). 1. Factor 1 (PC 1) is ri function of the si/c and pli! sicd maksup iit' ttw \\ :itcrslied. 2. Factor 7 (PU)is a fiiricrioi~of the opcratinr-iril histcir!, cifrincl hresr r!w 011 the watershed. 3. Factor 3 (PC3) is a fiiiiciioii srilcl' ot'ihc ripctaticinal Itistnr~.hur N iih ~IilfCrcnr results stemming from dit'frreiit ripcra~iciii\. In PC 1. total arca. strcani Iciigli. ~~ihs~rti~ct!,pc. ~Iraiiio~t'. iiild ~~c'c'j~t~cssare relatcd to nirbidity. Turbidity is posirii cl! c.orrc.l,tcd 10 cle\.i~tioiialdilï~rciiccs (steepness). area. and Stream Iengtli. 11icrè:ist.d stccpncs cnlirinccs tt~cc'rnc;ii~nal force of the water floiv. Iricreoscd strcriin Icngdi Icds to c~iiniilaii\c. sdiincrir Ioading according to the river crintinti~iin.Tlicre is nti iiicrtiisiiig clirinct. iil'scdiment and nutrient loading nith increasing strc'rlrn Ic'ngtll dw 10 hi$ crosion c'i.ci~sin some of the tnbuiaries as [lie? mergs oiic at a iimc into the iiiriin strcani. Turbidity. in contrast, is neçatively correiated to perceniage amof a natcrshéd diiniinated by metarnorphic substrate types. and by ndl-draincd soils. Here. siibsurfacs \vater flow is highly filtered. Terrain drainage depsnds IO a large cstsnt of the perrnability of soil. parent material. and bedrock. h~leiriiiiorphicbedrock tends to bc imre tjactured Table 6.5. Xlain factor table for PCX results for 16 \vatrrslicdç in New Bruns\\ ick. PC I PC2. and PC3 are the end product of a dimension reductiun escrcisc. (Tablc. 6.1). Parameter PC I PC2 I'C3 blagnirudc. Variance Prop. Total Area Stream Leiifth Clearcut '9 1-96 Planiation '79-81 Plantation '85-90 Harthood kIetaniorphic \\'cl1 Drained Eletaiion Dit'f. Tiirbidit' Conducti\ it) Total Pliosplioriis than igneous bedrock, due to repeatcd t'oldiiig O!' the hcdrock siructuic. and rtlated mineral and rock directionalit! that gi\.c \\.a!. to tiswrcs and cracks. In PC7. areas planted froiii 197cI to 1990. aiid SC) ti\\mi do~iliti;ltcd watersheds are negatively corrtt1att.d to total pliosplioriis in strcnin \t:itci.. III contrast. hardwood dominated \vatersheds art. positivril!. corre1ntt.d to total phosplioriis. Under hardwoods, soi1 pH values are liig1it.r \\ hich rediiccs 1' retention: Itaïhing o1.P into the river system would thcrefore be ii~crcnsed.Podzolic snils tc'nd to I;w,n iindcr sofiwoods - which is acidif"*ing- leadiiig to high .Al :ilid 1:c osirlc xcurii~i!a~ionsin B layers. Much of the otlier~viseniohilc P is tlicictorc stroiigly soil-3dsilibc.d in rhw layers. Vigorous forest stands (siich as densr. plant;itioiis ar ihc sii;iliiig >!;igc'l contribute further to on-site P reteiitioii Jiic to Iiigh r;itcs ol'nutrieiit ;ihsiii-prion. In PC3. recent clearciitiing ( 199 1 ro 1 W6) is positi\ cl!. ~o~~c'l;~tc>dio electrical conductivity readings ti~rtlic \i;ltcrslic'd?;:oIJcr pI;i~itatioiis( 1 O?') 113 1084) are negatively correlated. 6.4 Conclusions These three factors Iead IOst.\wal coiicliisiriiis. as lbllriti';: - Larger watersheds (\vit11 corrlispondiiigly longer strciiii nci\vorks. largcr rivers. and greater variation in topograpliy) tvill Iin\.e liiglicr tiirbidity \,rilucs. - Watersheds that are predoniinantly ivcII diaincd. \vith a tiactiired siil~stratethat does not encourage ponding at tliti siistace and \\.etlands \\.il] liaw lwei turbiditp values. - Increased softwood dominated armaiid increnst'd plantritions (pres~iniahlytvith sofiwood species) will decrease tlic pl~osplioriiscounts in strcriin \v;itt.r. Manu sofiwood species are sffecrivc at rctriiiiiiig ancl rc'q clin$ iiutiiciiis. p..ii.iicularly phosphorus. Hard~voodstend to be Icss nurricnt ct'ticicnt. so incrcised hardwoods could lead to increasrd phmphoriis in strean-i tt.atcr (Kiiiiii;ins. 1057). - Harvesting activities iiks ckarcurting cati lx rspcctcd to iiicrcasc icrii Ioads in streams. in turn increasing elt.crric;il coi.irlwti\,ir! in 511-crini \\,aier. [Iicw cffscts can be seen for ss\mal !.cars aficr lilir\csting (.Ic\ictt (II (11.. 1000). - Relativelg jpoung striiids (qed IO to 10 !cars uIJ I ;1rc yonii~g\.igcii.imsl!,. und taking up relativel!. Iiigh criricrntrnricm LI t' i~iitriciirs.l\'iitci.;lic~l~ wii li IN-gt. quantities of these !.oiing stailds it~ciiildiliercli~i-c Iic r.spc*c~t.ciIO Ji:,\ c Iiri\cr concentrations of ions iii ilic srrcani \\:llcr. i111cI tti~~.ct;)rcIiwcr r'lc~tric;~I conductivity mcasurrincnts. CHAPTER 7 MULTIPLE LhlEAR REGRESSIOX ANALYSIS 7.1 introduction One objectitt of this project was to find direct relationships benveen \vater quality parameters (dependent variables) and tvatershed cliaracteristics ( independent tariables). While factor analysis \vil1 identify general trends for multiple tariables. it doss not single out dependent tariables for analysis. Therefore. multiple linertr regression is used for selected uater quality paranieters to determine uhat \vatersl.ied cliaracteristics affect tvhich \nater quality parameters. The regression equations de\doped as a result of these analyses are listed in Table 7.1. including the standard eiror of the coefficients. Statistics for these equations are listed in Table 7.2. and the' are gaphed in Fi:. 7.1 and Fi-. 7.2. 7.2 Jlethods Sonie natu qualit! tariables tvere omitted. For esample. sodium and chlorinc are predotninantly deposited b!. atniospheric drposition. Xa and Cl nre mainIl. dsrit.ed fioni bedrock and atmospheric deposition. NH4 and K are c!-cled tmy quickl! tvithin the forsst stands: the size of thrsr ions also suggests thal the! t\.ill become trapped in the soi1 niatris. Both of these facts increase the difficulty of predicting thern. Mg is oniitted because concentrations are low (near detection limir) and it is assumed to be derived mainly from the bedrock. The total dissoivcd sotids measurement is derived directly from electrical conductivity measurements and has the same regression results (q.v. Ch. 3.6). In this context, TDS is redundant. O 0.5 1 1 .j -7 Turbidity - Predicted Total P - Piedictcd Si Od G 1 CLI Sk Th tir Fh C\i'BI; Qb Tr LI Ciln TOC - Prcdicted Total 3 - Prcdicrcd - -, 1.3 7.6 7.9 8.1 7 EC - Predicted pH - Predicted Hardncss - Predicted Calcium - Prcdictcd 0.05 O. 1 0.15 1.15 1 .3 I .4S Nitrate - Prcdicted Sulfate - Prcdicted Fi-. 7.2.Fitted versus dependant scatter plots for four mu1tiple linear regression analyses (Table 7.1 and 7.2) for sisteen watersheds in north-western New Bmnsivick 7.3 Results and Discussion Typically, sediment is Iieavil y iiiflucnccd b!. activities i 11an indus~rialforest. particularly through heavy road use and culvert failure. Tliers did tioi wmto be any strong correlation between sediment luad and an!, of tlit. independeni ~xiriblesused in the analysis. including road lençtli and density. and stream crossirig niiniber and density. There are four likely reasuns for rliis lack of correlation: Road use or road activity is not a variable in this dataset. Simply put. it is not known where and when thc roads depicted on the GIS intoriiiation slstem have been heavily used. in relation to grab san~plingdata. Becausc foresi ronds tend to settle quickly after periods of heal.! usage. \iindo\\.s of opportiinit! !O masure this effect may be short. Culvert failure at streani crossings is not a \miable in this datascl: 11 hile tlie raw number of stream crossings is gi~w.timing and location ot'possibk criliw-t failures is not known. Sediment particles are relatively dense amongst naier qualit! parriiiietcrs. The) tend to settle out quickly and may not be found at a distance lion1 !lie source of the sedimentation. Also. any lakes or ponds betuwi tlie sourcc of scdirnentation and the sampling point would in eflSct tilter out these scdiment panicles. Sampling protocol. Monthly grab saniples ivere taken at points douiistrearn of large watersheds. The relatively infrequent sampling \vould tend to miss high sediment loading events. The location of the sanipling sites ~voulddilute and filter any sediment that did enter the streaiu. simply because sedimeiit dues not tend to persist, particularly in higlier order. sloiv movin,(1 streams. Turbidity was found to be dependent on the pcrcentape area of hoth sedimentary substrates and poorly draincd soils (T'ab. 7. i and 7.7). The :iedinientrtry substrates are more easily weathered than iiietaiiiorphic or igncous suhstratcs and would therefore contribute more ioris to tlie strcani. Poorly drriiiicd soil5 Lire generally indicators of higher water rablrs. and are olien associatcd uirli soAsr. finer textured road surfaces (Brad!,. IWO). Bccausc of the high \ifatt.r tablc' alid soft road surface, puddles and pools tvould be prcscnt on the siirface tor greatcr Ic'~il,~hsof time. This would lead increascd splasliing ot'dirty \\atc'r into ditclic's aiid ~lii~sinto the Stream. Though a macronutrisnt. phospliorus is iiot as ahuiidaiit as nlost iil'its brethren. It does. ho\vever. beconie niore soliibls 3s the pH ;~ppro;~chcst~ci~tral. The streams esamined in this study in\,ariahly ha\^ pH lues het\~ec~i6.5 ml 8.5. The regression equation (Tab. 7.1 and 7.2) s1iou.s tis.0 \.ariahlcs nssixi:iicd itr rota1 phosphorus. These are sedimentary siibstirites riilcl pcrcciit 01'3rc3 plaiilcd hetneen 1991 and 1996. Again. the sedimentar!. s~ibstratesarc mort casil!. \vc.:iil.cied than other substrates and therefore simply contiibiitt. niore pliosphoriis tri ~licstrcarn. The positive correlation to ment plantations is niost likcly an i11dic;itiori ol'iite disturbance. Any site preparation or scaiilication done on sites to be plaiitcd in that time period would affect the phosphorus le\&. pai-ticiilarly bccriuse it is soluble at these pH levels. Even if site preparation \vm mininial. simply condiiciirlp activities on these former clearcuts so soon after tlic harvest ~vouldincrease tlitir susçeptibi1it)o to phosphorus losses. Total organic cwhon and total nitrogcn arc strongly correlatecl \.ariables. Each affects the other and they tend to reniain proportional in tlic: laiid>cape according to a Carbon to Nitrogen ratio. This ratio iiiay froni \vatcrslir.d to watershed, but with watersheds. C and K are strongl! rclated. Botli I-ia\,c strong correlations to percentage area covered b!, organic substrates and to pc.rccnr area pIanted between 1985 and 1990 (Tab. 7.1 and 7.2). h'itrogcn rila!. bc fi11-tlic'r influenced by other factors. as indicatd by its sonie\vliat wrakcr correl;ition to organic and plantation independent ~xiablcs(Th. 7.7 ). Softwood plantations estnblishccl bct\vt.cn 1985 and 1990 \vciuld Ii~i\.e averaged approsimately ten !.cars ol'agc. diiring the sarnpling pc:riotl i~scciin this study (samples were taken froni 1995 IO 1998. incliisi~~c).Tcn-year-olci pl;intations tend to be vigorously grotving. rapidly [;king iip niitrit.nts. Forcst tlooi Ia!crs ~vould shrink as uptake and decomposition incrcased. and c\xpotranspirritic,n \\oiild generally be high. Plantations of this age rire also ofteii rissociarcil \viili Iiiglier soi1 perrneabilities: a greater perccntage of'surtacs \\.att.r \\.ould penctratc 111~: soi1 layers and emerge via deep percolation in the strennis. Carbon and nitrcycii t\orild be filtered out of the \vater by this proccss. The arnount of thc tvatershed cwwed by organic suhstratcs lias LI direct effect on the amount of carbon in the sircams because it is n soiircs of rirpanic carbon. Because of nitrogen's afflnity \i-ith and corrclntion tu carbon. total nitrogen levels are aIso affected by this influs. High quantities of carhon and nirrogen crin be tied up in organic soils. The acidity (pH) of forest streanis is vcry importaiit both to aqiiritic life and to many geochemical processes. includiiig the solubilit!- and a\rriilabilit! of'ii-iany other minerals. The percent area of the uaierslieds co\,esed b!, scdiniciitar!~subsirate and the area covered by non-forest land-types seetil to ha\t the grsatest iinpact on pl4 (Tab. 7.1 and 7.2). Becausç of the sedimentar! rocks' increased caphilir y for weathering, more base cations are released into ihe stream. increasing thc pH. Non- forest land, however. tends to increase surface runoff bccauss tlicse Irincls (tirban. ucrave1 pits. agriculture) are less pern~eableo\wrill than t'oscsts. The iiicscased surface runoff into streanls leads to grridual aciditication b!. reducing tlic m«unt of filtration and buf'fering (froni soils and s~ibsoils)on \\.ater entering tlic stscarn network. Electrical conductivity (or total dissol\ai solids) depends cntiscl! on the ion count in the stream. Any impacts oii strcaiii clicniistry tliat \\~oulciiticrcase ion concentrations ~vould- at least to sonie dcgree - increase the EC scaciings. The three variables determined to have the greatcst et'fcct on EC are inctaiiiorpliic substrates. softwood coverage. and non-forest arc1 (I'ab. 7.1 and 7.2). hlctanioipliic substrates are negatively correlated lvith EC. presiiinlibly bccriiise ot'ilic dit'iicult! iri weathering them and in their leu. soltrhle ion coilrent. Sotiuuod cowrust. (kvhich is positively correlated) wvould tend to indicrite incrcased podzolizaiion (aiid therefore increased leaching). Sofiwoods also geiierally prodiicc conditions thrit are more aggressive in soi1 tveathering. The effeci of surface runof'f due to non-forest area would in this case decreasc base cation content derivcd froni deep percolation; the runoff would still contain acidifying orpanic elenients. Hardness is negatively corrclated to tlwe independent \xinhlch: metamorphic substrates. non-forest arcri. and srrcani drnsity (Tab. 7.1 mi7.2). Metamorphic substrates are hard to n.t.ather. and as tlicy increasingl!~cioniinate a watershed. base cation concentrations (Ca and Mg in paniciilar. as (lie! arc the source for the hardness calculation) decrc~isr..As has bscn prr\.iousl! st;itcd. non- forest area. due to the greater proportirm ol'surtace ruiiot'fand its iiicre~i~cd acidifying particles/decreased base cation contcnt lo\vers hardiicss as it iricicrisss. Stream density decreascs hardness as ii iiicrcascs for [na possible rcnsims: one. increased streams per hectare \voiild ciiliirc ilie n.ater. cicciciisiiig Iinrdiicss Ic\ds: and two. watersheds uith lo\ver strcan] dciisitics \voiild liri~egi.c:itc.i. SLI~SII~I~IC'~'tlmv. III other words, a low Stream dcnsity \vatcri.licd \vciiild indicatc that ~iihsiirl~icc\vater would have to flow throiigh soi1 and btdrock tiirtlicr tlinti ;I \\.aicrshcd I.\ itli liigli Stream densities. wherc streanis ti.oiild hri~~to IV closc'r rogcthcr. Calcium concentrations in strcrini ivatcr ssciiis io bc prcdictriblc' \\ itli tlirec variables. sedimentary substrates (positiwl~.correlated!. Iiard\\.ocd cm crrist. (negatively correlated). and non-foresi ;ireri ( negiiti\.cl!. corrt.lateci ) (l'able 7.1 and 7.2). Sedimentary substrates \c.ould bc :I source of calcium due to tlieir casc of weathering. The hardwood forests it is rissunicd gron. on t'riirl! rich sites and take up large quantities of calcium. thereb rediicing Ca Isvels in the strcanis. I\;oii-forest area would have the same effect on Ca tliot it lias on EC..pH. and Hardncss - it simply increases acidi-ing elements \\ I~iledecreasing base cation (Le.. cdciiim) concentrations because there is more surface r~iiioffassociatedn.itli it. Sulfate concentrations in streanis are correlatcd \vitIl ~rcll-draincdsoils (negatively correlated). forest coveragc (positi~dycotrelatcd). and tlic ii-iitiinium elevation of the watershed (positi\,ely correlatecl) (Table 7.1 aiiJ 7.2). U'cll-Jrained soils are generally indicative ot'increassd pocizolization: thesc conditions increase the absorption of sulfate. dong ivitli aliiniiniiiii and iroii osides (Rrrid~.1090). It is thought hotvever that minimum elevation and torest co\.cragc could tir: demonstrating the same effect. Cloud aiid fog interception is knoun to iiicimse both ivith increasing elevation and witli incicriscd !'orest co\.c'r;lgc ( Yi 11 anci ..\ rp. 1994). Fog or cloud panicles are generrilly niiicli Iiig11t.r in sultritc. mi niri;iic ctiiwcntrations than rain- or sno~vfall. Nitrate concentrations sceni tci 1ia1.c. sonic of the smc' ct~rt.cI,îtioils;la sulfate concentrations (Table 7.1 aiid 7.7). Ho\tc\w. nitrarc is rtiorc. sircingl! corrclaied ii.ith moderately drained soils (negati\.t.l! a'; \vt.Il as iiiii~in~~iriiclctxtitin (pcisiii~el'. correlated). Minimuin elevation \vould incrcasc cloiid aild tiis ititc'rccp(ic)il. increasing nitrate deposition. Forcst coiwrigr: does nui swii to bc ciirrclrircd because nitrate would be heavily iiti1izt.d by tlic 1rct.s tliat increascd iis intcrccprion. effectively balancing out the ettect on strcnni \tuer coiiceiiiiatioils. :\lso. moderatel?. drained soils are assunird to bc correlatcd because iiiidcr thrise conditions. nitrate disappears (Le.. it is converted to N2and nitrite and gets taken up inimdiately by the vegetation). 7.3 Conclusions Overall. substrate type seems io have a significaili impact on iiiost of' the variables examined with multiple linsar rcgressioii. Son-forest arttri also secnis to be important. indicating that increased coinwsioti of forest land to ilo~~-fi~rc.stiistts !vil1 have detrimental effects on stream Ivntcr qualit!,. Pliin~ritionsnia! riff2ct strc'ani anion concentrations to ~wyiiigtwsiits. t7di b~causc~of' the disti~rhmc~1Iit.y in~ply and the vigorous grou-th often resiilting ITom tlieir prwiice. It is ditficiilt to state with certainty that the relationsliips dc.t.t.lriped u ith niultiplt. liiiCar rcgrcssion are certain due to the lirnited smpk six. 1 t'conihinccl nith otlicr siiiiilnr siiiclics. it couid increase the certainty of lhs rclgressirins. I'cibliçl!. ai~ail;iblc.GIS CO\c'rag~s are particularly gvod in Neiv Bru~wvickand iwst ri\w nioiiirnriiig stiidicis \\oulJ sliare some water quality paraniettrs siich 3s iiirbiclii!.. pl-i. sedinieiit Id.etc. CH.4PTER 8 GROI'P IMPACT .4NAL.YSIS 8.1 Introduction A general Iiypothesis \vas developed rcgarding cspcctcd cuniiil;iti\ c. input effects of land-use and strcam crossing density on \iatcr qualit! in ti~iirtli.t;f'th. and sisth order streams (Table 8.1 ). This I~!.potliesis statcs tlint as the totril ai.c:i cit'non- pnstine forest in each \vatushed increascs. nacr q~irilii! slioiild dtcrcriw. Non- pristine forest is detïned as tlis suin ol'total non-torest am(urbail. ~it,rriciiltural. industrial. or other usage). arca of clclirc~itsmer a sc\~.itccii!car ptsii-rci ( 1979 to 1996). and plantations ovsr tlis sanie se\ eiiteen !car pcricid. S~rea~iicrcwing density is defined as the number of streanl cros.;ings per \iatcrslicci dividcd h! \iarershsd size in square kilometers. Table 8.1. Water quality parameters ;uid prcdictcd cl'lkcts ol'ii1crcast.d laiitl-use area and streani crossing densii!.. Parameter Predicted I!fti.ct Obscn cd 1: t'tbc~ Turbidity Ii~crcase Sont: Sediment Load IIICK~~SC Sonc Total Organic Carbon Incrcnsc. honc Electrical Conductivity Increasc s1inc PH Iiicreasc or Dccrerisc Soiic Hardness Incrcase Konc Sulfate Increxse None Nitrate Increasc Nonc Total Phosphorus Incrcrise Noi~t. Total Dissolved Solids Incrcasc Noiic Sodium Incrsase Noiw Potassium Incrcnse Nonc Calcium Incrcasc None Magnesium Increase None Chloride Incrense Kone Ammonium Increasc None Total Nitrogen lncrcrisc Sonc The basic rationale is bascd on the theory of cumulati\.e effects that statss as more sources of pollution (point. line or area sources) are added to a streani. cumulatii.e effects ivill increase. AdditionalIl.. there is an espected increasz in upivelling groundnmr as the \\.aterslicd increascs in sizc. ln gencral. up\veIling tvarer ~villha1.e higher pH. Ca. Mg. K. Na. and concentrations dien surfixe ivater ninoff due to soi1 and subsoil iveathering. In coiitrast. upivelling utsr ~\.ill hatx zero to near zero Isvcls of TOC and scdiments. .-Inother concept. that of tlir rjiacontinuiini, suggests tlirit a \iatershed is part of the sanie mwall unit but processes ciiffer indi\-idually drpendirig on thc section of the riia flint is béiy obssn~d.Accordin2 to Johnson PI (11.. ( 1995): "The coricept (of the riiw continuunl) states ihat tortisred riim s!.stc.nis ha1.c a longitudinal structiire rhat rtsults frorn n gradient of physical forces thnt change predicrably alonp the Icingth of the rii.er.'* First order streanis are erosional zones. \vit11 fast moi hgivnter and rclari~d!. liipli turbidit ~,alues.Higher order streams tend to brconis slo\~.rrnmin~ aiid cltarer as panicles settle out in the sloiver \vater (Fig. 8.1). Other processes \vil1 dso affect the Iiiglier order streams. cg.. ivetlands tiiter the n.ater that flo\vs through them and Iakes or ponds may act as sedimsnt traps. Hm.?metals and sedinients in panicular \vil1 settle out in lokes and ponds. When combined these tivo concepts can add to the difficulty of predicting fhp~eeftects. In essence. the river continiium concept illustrates how ivater quality High Streain Order Low Streain Order Water Level -dl \dimyt\ --- -- - Streamt Bed parameters become diluted as the obsenw rnmm dou.nstrcrim into 1iiglic.r order rivers. Dilution effects niay niask cuiiiiilriti~.eet'fects. Iri ipieral. dilutinri ciYects are directly but inversely proportional to \vatershed arca. 1.argt.r \vatt.rshcds Iiri~ta proportionately greater ability IO dilute sti-eam pollution tlian snialler \i.;iterslieds. 8.2 Mcthods Average values for \vater qualit! paraiileters \\-csst'iitc'rcd into a cl;itriset in a statistical package (Statvie~v8). r-Ilsu cnrcrcd into the clmbasç \vcrc' i\\ii pliranieters derived from GIS. Ttiesc \vt.rc land-use mi strcam cmssing dcii~it!.. I.;i~iJ-ussis an amalçamation of clearcuts. plantations iintl othcr non-tiircst iisr~ge'sc~c'liicting natural features like ~vetlands.Strcnni ciossiiig densit!. is citri~c'ddireçtl! l'rom ihe GIS system and is drpsndant on 1u.o I2rit~iresol'tlie GIS - rciricis ririd sti'caiiis. .A[ every intersection of rnad and strsrim in dit. GIS. ri stwini crossitig is ri~lJ~dKI the coverage. These crossings ma? consist ot'cultwts or bridges. Simple linear regression anal~sisiiscs CIsingle indcprndent \-riri:ihlc (in this case either land-use or crossing densir!.) to prsdict a single dependrint \.iii.ialile (any one of the water quality paramctcrs nicasuied iii this casc stiidy). Regrcssions ivere repeated for al1 water quality lariables \.ersus land-lise (Table 8.2). and again versus crossing density (Table 8.3). Table 8.2. Simple linear regression values for hrpothesis testing with total land-use (OO area subjcct to harvesting and plantations. urban. agricultural. industrial. and otlier non-forest area) bersus 17 \tater qualie parameters. Intercept Land-use Paranieter Regn. Coeff. Std Err P-Valut: Regn. Coeff. Std Err P-Valut. K ' 2 Turbidie Sediment TOC EC PH Hardness SO4 NO3 Total P TDS Na li Ca Mg CI NI14 Tord N Table 8.3. Siiiiple lincar regression ialurs for hjputliesis testins with strrarii crossing dciisit) (= of crossings lini2) Lcrsus f 7 wrtter quality panmeters. 1iitt.rcept Stream Crcissing Densit) P;ira~iicter Rem. Coeff. Std Err P-Value Rçzn. CaetT. Std Err P-Valut: R '2 Turbidiry Sedinient TOC EC PH Hardness SO4 X0.î Total P TDS Na K Ca Mg CI NH4 Total N 8.3 Rcsults and Discussion Using simple linear re=rcssioii to test cacli prctliction. it \vas tbuiid thnt there was no direct statistically signiticanr (u.= 0.05) t.t'ft:ct on \rater cllialit! parrimeters when compared to land-use (Table 8.2)ai. to smnm crossing densit!. (Trible 8.3). Other watershed processes couId be niasking the eRects of land-lise and stream crossings (Moldan and Ceniy. 1994). Iiriwc\w. \\.hich Icct to the use nl'niiilii\.ariate statistics to search for relationsliips bet\it.cn \vater qualit!. rind u.aterr1ic.d characteristics (set: preceding chapter). l'he t'xtcnt ol'non-pristine fi)i.t'?;~nia!. ahbe a factor in the lack or sigiiiticriiit corrclaiirin. hlost oi'tlic \\rttt.rslicds ha\c Icss tlian 25% land-use. Only one twc'rshcd (SwlIl Brriiich Kt.dg\\.ick Riwr) cscccds the 25% limit by any amount. This \i~ütcrslicdIlas a land-11s: pci.cenr;igc o 1'-I 1 .S'!o. ivell above the noml for that pan or Kt.u Hri11i4;wick. The degree to wliicli tlx \vatc.rshcds 1m.c beeii alicrcd I'rcini a so-ciiliccl pristine state could also be a major fricroi. in rc;ords i« the lack ol'correlriiioii bc~\i~.enland- use and the water qualit!, paramciers. ll~istt\xic'i-sheds ha\^ lcss tl~nii25"~ land-use: only one watershed excecds this anlotirii (Sriiith Branch Krdg\vick. ni 4 1 .Wb). Because scenarios such ris 100% or GVh \\we not tcsted. it is unknn\vii \\.lieiher or not those levels ~vouldha\.e direct elleçts on tvater qiiality. It is hypotlicsized. however, that the effects \voiild becori-ic apparent ai liipticr Ie\,els of'land-use. Stream crossing densit!. is sirnilat- to Innd-use in tlint the rixr çoiiiiiiiium affects how dilute the water quaiity parriineters have bscome bt'fore reacliing the monitoring station. In keeping ii,itli cui~iulati\-ceffects tlieory. howetw. as the Uexity 9f cr~csingcinrr~xes. the cuiniilnti~tcflécts siinuld become iiicw apparent. When considering strearn crossings. the cuiiiulatiw eft'ccts thcor! is Iess likely to hold truc because of the nature of rlic priiiicles releascd dut. to Stream crossings. The major impact of streani crossings cunies in the toriii of sedinient Io~dincreases. These sediment load increases are strotigl!. correlated to [lie estent of'~iscseen by rhct road that crosses the streani. .As road usrigc increases. gcnerrilly speaking so to does the sediment load added to the strcani Lia the rond crossiiig. Wearher also plays an iniportant rolc becaiise prwipitatiun n-iakcs tlic sediment particles niore niobile on the road surSricc iiicrcasing tlic ~~ii~iot'f'i~itothe smarn. DUC ta their densit!~and mass. seditnent p;irticlt.s do no[ rcnd IO rcinniii in s~ispmsionfor very long after entering 3 streani ç~.stc~ii.Rccriusc 01'1his. ii-ioniroring iti hi+r order strearns will tend to miss sedinleiir luads. as nill ntontlil! qths;iniplc\ II 8.4 Conclusions Measuring the impacts of ciirnulriti\e eftkcts ma!' reqriire niore et'lbiz IO realize than simple monthly grab samples. I-io\vcver. e\.en if the srinipling int.cliaiiisin were altered to provide a greater chance of tinding road-use effects. the percciitqe land- use in the watersheds may be too lou 11) slio~vany ef'fccts on tvater qiialit! rit this scale. CHAPTER 9 SUMMARY AND CONCLUSIONS 9.1 Original Contributions The analysis of the watershed characteristics and tvater quality data for 16 watersheds in New Brunswick was developed and completed. 1. Mapping and Landscape Characterimion. The \vater monitoring locations in each watershed were transferred to a GIS niap and used to delineatt. \vatershed boundaries with a digital elevation niodel. Relevant information \vas estracted from within each watershed boundary per river. This information \vas summarized and standardized for use in the analysis. Types of inforn~ation estracted included land-use. streani crossings. torest covrrtype. river and road networks. and topography. 2. Water Quality Database Applicatioii. The \vater qiiality data collectcd over the length of the study period \vas arnalganiatcd into a single database. This database was custornized to provide specitïc database monitoring tools for the end user. including custom user-defined graphical and tabular reports. Monitoring ot'\i.ater quality results \vas simplified with the use ofbuilt-in statistical fiinctions and a water quality safe-level reponing mechanism. 3. Analysis. Water quality and land-use data were analyzed to search for patterns and relationships between the water quality parameters and forest operations or land-use. Methods used included correlation analysis. principal component analysis (factor analysis). multiple linear regression. and simple linear regression. 9.2 Analysis Conclusions 9.2.1 Correlation Correlation analysis did not dernonstrate niany direct relationships between land-use and water quality. It did. ho~vever.illustratc relationships bctwcen di fferent biophysical watershed characteristics !vatsr quality. The relationships liiund include: percentage areas of igneous. sedimcntary. and organic substrates art. positively associared with percentage areas of poor and moderate drainage: thcsc percentage areas of substrates are negativcly corrclnted \vit11 perctntrige areas of good drainage: peccentage areas of metamorphic siibsrrritts arc tiegati\.el! correlatcd with percentage areas of poor and moderate drainage, and positivcl>-correlatcd \\ith percentage areas of good drainage: percentage areas of clearcutting and plrinting rictivitics rire (for various time periods) correlated to \vatershed percelitage rireas of hardwood/soft\iciod contents as part of the local forest nianagerilent strategr: changes in correlations between pcrccntage arcas ot'soft~voodcontent and percentage areas of clearcutting in fivs ycar periods suggests ihat hawesting operations have become more targeted totvards \vatersheds tvith mort. sofbvood area; hardwood area dominated waterslieds haipehad Less clearcutting over the sarne period; r plantations seemed to be targeted to~vardsnatersheds nith high percentage areas of softwood, presumably because the plantations bvere placed on old clearcuts. Increasing levels of percentage area of igneous and organic substrates. total watershed area, and arnount of open watcr per watershed lcads to dccreased strearn water pH. Therefore. while pH values might currentl not drop bdow critical threshold values for aquatic life in the rivers. acthities that might contribute to basin acidification (conversion to softwood forest by plantins. for esaniple) should be avoided in watersheds with a high degree of uetlands and with an igneous substrate. 9.2.2 Factor .4nalysis (PCA) Results from factor analysis (principal coniponent rinal>.sis)siiggest seiwal possibie relationships between water qualiry and land-use: Larger watcrsheds (with correspondingly longer stream nernorks. larger ri\.ers. and greater variation in topograph!,) uill ha1.e higlirr turbidit' \cilues. Watersheds that are predominantl) nell drriiiied. presuriiabl> lia\ in2 a i'ractiired substratc that does not encourage ponding and \vetland forniation will ha1.t: lower turbidity values. tvatersheds with increased softwod dorninated areas and incrcased arcas ot' softwood plantations \vil1 decrease the phosphorus counts in stream water. Man!. sofiwood species are effective at retaining and recjding nutrients, particularly phosphorus. Hardwoods tend to be less nutrient efficient. so increased hardwaods could lead to increased phosphorus in stream water (Kimmins. 1987). Hawesting activities like clearcutting can be espected to increase ion loads in streams. in turn increasing electrical conductivity in streani water. These effects cmbe seen for several years after harvesting (Jewett et cd.. 1996). Relatively young stands (aged 10 to 20 years old) are gro~ving\igorously. and take up high amounts of nutrients. Watersheds lvith large quantities of these young stands would therefore be espected to have reduced ion concentrations in Stream water. and therefore lower slectrical conducii~.itymeasurements. 9.2.3 Multiple Linear Regressioa Using multiple linenr regnssion. scvsrnl regrcssion equations ivert. produced that had relatively high R' values and crirrespondingly iiiyh signitïcancc \dues (i.e.. low P-Values). Turbidity is a function of area of sedimentary substrate and of pourl!. drained soils. Both factors increase the content otparticles in strcani 1vatt.r. Titrbidity = 0.656 + (0.Oij*% rlrw .Cediliritenroi:i*Suh.vn.crrr) + (O.06-3 *% .irecl Poorl}~Druincd Soil): R' = 0.-2. P- INIIIL' = O. 0003 Total phosphorus is depcndent on the estent of sedin~rntarysubstrates per basiri and estent of basin coverage ofrelativcly rrccent plaritatior~s.Sediincntar). substrates weather more easily than igneous or metamorphic substrritts and thus are n primary ecosystem source for phosphorus. Rscent plantations probabl). release P from soils because activities thar lead to soit disturbance that leads to P mobilization. Total P = 0.007 + (0.00012 *% Aretr Sediiiiciîtm?. Siibstratr.) + (O.OO4 *% .-!ren Pimted '91- '96); R' = 0.77, P- I,Gdrte = O. 0001 Total organic carbon and to a Iesser extent total nitrogen are functions of basin coverage of organic substrates and % area of 5-1 5 year old plantations. TOC and total N are highly correlated variables to one another. but TOC has tlic stronger relationship to water quality between these two variables. Organic substrates are a natural source for TOC: vigorously groning plantations a~~eragingIO years of age would increase AET levels. decreasing the \vater table. reducing surface wet spots and decreasing TOC levels. TOC = 4.62 + (2.43 *% heu Ot.gmic Suhs/rrrt~)- (0.813 *% :lr.rtl P!m!etl '83- '90); R? = o. 2. P- r,nfl,e < o. ooo r Hardness seems to be a function of three factors (al1 negati\dy corrrlated): metamorphic substrates. non-foresr area and stream dcnsit!. h4etamvrphic substrates do not weather easily and contribute little to in-streani CaCO; Irtels. Runoff from non-forest area is generally acidic. As stream drnsity increases. the proportion of deep flow water that reaches the srreams is rcduced. dccreasing the overall CaC03 concentrations. Hardmss = 244 - (O.477*% clrect .\ktmior.pliic S~th.so.u/e)- (1.?80*% .\'on- forest drea) - (803O*Strm11Densitj.~; R' = O. -j. 1'- Idilr = O.000- Sulfate is seemingly a function of eleva~ioii.forest cover. and iveIl-drained soil. Most sulfate in forest environments is froni cloud and fog inputs. and ele\ation and forest cover affect hon*much cloud or fog is intercepted at a site (Yin and Arp, 1994). By contrast, increased basin ccn'erage of tvell-draincd coiis \vil1 generally increase adsorption of sulfate (Brady. 1990). Sttlfnte = -7.41 + (0.002 *Eh:) + (P.j7*Foresr) - (O.O@*lt i.11 Dwined); R' = 0.85. P-Value < O. 0001 Nitrate appears to be a function of slevation and moderatel). drained soils. Increased elevation would increase cloud and fog interception (Yin and Arp. 1994) and moderately drained soils lvould facilitate uptake by vesetation. Forest cover is not a factor with nitrate. presuniabl!. because of intense vegetative NOj uptake for al1 tree covered land surfaces. These natersheds seem to bt: SOa saturated (SOJjust moves through the system) and NO; unsaturatcd (NO3is removed by the vegetütion). Nirrnre = 0.095 + (O. 0004 *Elei!otiuti) - 10.002*% -41-corf.\.lodcrvrreI~~ Druinrd Soil); R: = O. 71, P- l'he = 0.0003 In general. clearcutting does not seem tu be a Gctor in an? of tlic regression equations. Plantations do seem significant for some u.ater quality \.ariables (total P. total N, and TOC) either because the! reprtisent an increnssd disturbancc regime or because they are associated with ~*igorciusgronth. hlost of thc ivatcr qurility can at least partially be predicted \vit11 abiotic f>çtors such as substrrire type. drainage. elevation. or stream density. Forest co\w and non-lbrest land uses also esplain part of the overall streain qualitp variance 9.2.1 Impact Analysis When water quality parameters are esanlincd directly against land-use and stream crossings using simple linear regression. no statistically significant results are found. However, land-use percenrages in the watersheds analyed do not represent a completely comprehensive summav of the possibilities. AI1 but one watershed have 7j% or more land in a "pristine" forest condition: that is. the forest has not been disturbcd in at Isast 20 \.cars. The estent of operations could be too Io\\ to show up at the scales being anal\.zed. Also. it miist bs noted that safe !vater qualit' parameters for aquatic ~vildlifeare not esceeded on these ri~rers. Stream crossings do not haive apparent effects at this scale for se\.eral reasons: 1. Road use or road acti\.ity is not a variable in this dataset. 2. CuIven failure or bridge u~ashoutsare not accounted for in the daraset. 2. Sedinient panicles st-ttle out quickly in areas ofslo\r or constrained moiunent suçh as lakes. ponds. nidmed streanis. or dams. 4. >.lonthly grnb samplcs takcn infrequently at arbitrary tin-ics far rc1noi.t.d froni 3ctii.e t'orest operations do not capture the sedinient tliat does niake il tu tlle scimpling points. 9.3 Rcconirncndations .Arictl~~sisresults suggsst that forest operations (plantations and to a Issser c'stem clcarcutting) do affect \\.ater qualit!.. Ho\veitr. the effc'cts of fortstry arc' tliought to be partially masked by othsr landscape factors at these scales. The masking effect of abiotic factors combined ivitli relati~.elylow occurrences of non- forest land-use and a sampling method not conducive to capturing the effects of Stream crossings makes direct links bettveen forestry and \vater quality difficult. Multivariate analysis rnethods provide a means to tvork around this !imitation by accountin= for factors other than land-use but only to a limited estent. To deal with this problem. it is recommended that one of three niethods be chosen: sampling methods be altered to include sedinient traps in evcry riw 31 each monitoring station: these traps have to be analyzed (eniptied) freqiicntly to determine when rnost of the sedinient loads \vert: added to the streani: sampling points be added upstreani from the niain sampling station to pick up on sediment loads before they settle out: collect samples at times chosen to coincide u.ith periods of hca\.y road lise. road mainienance. and precipitation c\.snts. Sediment impacts on strearns can be sawe. Sediillent cm directl. inttrfere tvith freshwater aquatic life. and can also st'rt~3s a L.cctor for cheinical coinpoiinds thar are inimical to aquatic organisms. Loggin- roads and skid trails 3rc tliought to contribute to over 90% of the sediment causcd by lorcstr>.The follo\i.ing guidelines (taken from Wençer. 1984) are suggested to help reduce the risk ofst.diinentation in streams: Use buffer or tilter strips Select logging methods and equipment to niininlize roads and reduce disturbance in soils and stream channels. Minimize the grade on roads and skid trails (below 10%). Provide means (cross drains and broad-based dips) to channel movin,o \vater from roads and trails. Minimize dee~disturbances such as ruts during logging. As much as is possible, avoid road and skid-trail construction. and road maintenance (such as surface grading) in wet weather: dso. do nor use skid-trails constructed on easily compacted soils during ii~titcathcr. Miriimizc the rime spent in each area. as well as the number of roads and trails. Upon completion of logging, decornmissioii roads. remove tenipomry bridges and culverts and revegetate problem areas. Otlier than logging efïects. abiotic factors arc \w>rimportant u ith regards to stream water quality; thereforc. climatological data slio~ildbe addcd to the database to make the analysis more complets. IdraII!.. cliniatc-iiionitoring quipiiienr ivouid be included ai each monitoring station. D~icto the csptnse itn 011-ed. it nould be more efficient to locate one climate nionitciriiig station in each rcgion. \\'ct deposition analjtsis u.ould provide clues to thquantit!. of certain nutricnrs arc. heing added to the watershcd annually, .4ir tempmture recclrds could bc contrasred ayainst stream temperature records. Stream teinperaturcs sliould tlwcfort. dso t-ie included in the database. in a format conipatiblc iviili orlier data sources. Giah saniplc: temperatures woutd be biased by the diurnal tmperaturc cycle ol' ttach sircarn. Continuous remperature records from dataloggers placed in the strcanl could be used to derive average values for the time pericid coincidine nith grab sarnpls records. However, care must be taken to ensurc that records are wailable for al1 sites and al! time periods. Landscape pattern may also hais an effect on wrer qualit) parameters (Hunsaker and Levine. 1995: Jolznson er cd,. 1995: Omerink er al.. 198 1 : and Tufford et al.. 1998). The introduction of metrics such as prosimity. doniinance or contagion to the GIS analysis could improve the prediction of water quality parameters (Hunsaker and Levine. 1992: Onirrink et al.. 198 1 : and Tufford et (il.. 1998). Any improvements in the precision of GIS data will increase the reliability and accuracy of water quality predictioii ( 1-Iunsaker and Levine. 1992). According to Osborne and Wiley (1988). land further awy fi-oni streams has less impact on water quality. The continued monitoring of tlw \intmheds \vil1 provide a set cit'bascline data for future study. particularly if major çhmpcs in land-use occiir in an' of the watersheds being a~ialyzed.With rhis tlicsis. Iorcst managers niüy \vis11 io use: 1. tools to nioniror water qualit' .- 11. to get predictk~etools for water quality in al1 forcst \vatersheds ... III. to espand the current water quality iiionitoring effort and direci it tcnvards areas thar are sensitive to land-use cl~anges. Potentially supplemented \vitIl other riiialysis tools. forest managers \vil1 be able to prcvent -or ar least derecr - detrimental changcs in \vater qiiality before the!: have a chance to harm any part of rhe ecosystcnl rhat depends on thrse sisteen riIrers in New Brunswick. LITERATURE CITED Abacus Concepts. Srtrti.ic.w R~firriice.Abacus Concepts. Inc. Berkele!.. California. 1996. Anonynious, 1985. Hearing about [vater: A synthesis. Inquir!. on Federal Cj'ater Polit!, Ottana. Canada. Cat. No. En 37-71 11 985. Bell. 14..;\.hl.. Broum. J.M.. and Hubbard. W.F. 1974. Impacts of har\.esting on torcst environmenis and resources. Can. For. Sen.. For. Tech. Rep. 3. Bron.nlo\v. AH. 1979. Geocheniistr!.. Prentice-Hall. Engleii.ood cliffs. N.J Canadian M:ater Qualit! Guidelines. 1995. Cronaii. C.S.. Pianipirino. J.T.. and Patterson. H.H. 1999. Iiitliience of Land Csc and If!drolog. on Esports of Carbon and Nitrosen in a 11aine Ri\u Basin. S. En\.iron. Qud. ?8:953-961. Crosbie. B.. and Cho\v-Fraser. 1'. 1909. Pcrcentagt. land use in thc \i-;itt.rslicd dcrcrriiiiics the \vater and seditnent qualit! of 22 niarslies in the Grcrit Lakcs basin. Crin. J. Fisli. Ayuat. Sci. 56: 1781-1791. Duuglas, J.E. 1971. :\nnotatrd bibliogriipliy ofpiihlications on nmrshcd niriiirigcnirilt hy Soutlislistm Forest Esperinient Station. 1925-1970. L'SD.4 For. Sm.. Rts. I'rip. S E-93. Driscoll. C.T.. Likcns. G.E.. Hcdin. L.O.. Eaton. J.S.. and Bormann. F.H. 1989. Chringes in the clièniisrr! of surface nmrs: 25-year results at [lie i-iubbard Brook Esperimental Forest. NH. En~iron.Sci. Technol. 23: 137-143. En\.ironniental Systcms Research Institute. 199-1. Arclnfo data iiianagenient. ESRI. Redlands. CA. Frink. C.R. 1991. Estiniating nutrient esports to estuaries. J. Enliron. Qual. 20:717-774. Grip. H. 1982. Water chemistry and runoff in forest streams at Kloten. Uppsala University. Departnient of Physical Geography. UNGI Rapport Nr 5s. Hrnktt. J.D. 1978. b: Stanley and Arp. 1998 (q.\*.). Horton. J.S. 1973. E\.apotranspiration and ivater research as relatcd to riparian and phreatophl-te manasement: An abstract bibliography. USDA For. Sen.. Misc. Pub. No. 1234. Hunsaker. C.T. and Le\.ine. D.A. 1995. Hierarchical approaches to the study of nater qualit' in rii.ers. BioScience. 45: 193-202. Jetvett. K.. Daugliart!.. D.. Krause. H.H.. and Arp. P.A. 1996. %'atershed responses to clearcutting: effects on soi! solutions and Stream u.ntttr discharzr in central Yeu Bruns\vick. Can. J. Soil Sci. 75: 475-490. Johnson. B.L.. Richardson. W.B.. and Naimo. T.J. 1995. Past. present. and future concepts in large ri\w ecology. Bioscirnce. 45: 134- 14 1. Johnson. H.J.. Cerezke. H.F.. Endean. F.. Hillman. G.K.. Kiil. X.D.. Lees. J.C.. Lonian. rl.X.. and Po\vell. J.iM. 1971. Some irnplicarions of large-scale clearcuiting in Alberta: ,A literature re\,iei\.. Nonhsrn Forest Ressarch Ctntre. Edniontoii. .-\lbert;i. C'an. For. Sen.. Info. Rep. XOR-X-6 Johnston. R.S. 1984. Efkct of small asprn clerircuts on uatsr !.ièld and ivritcr qualit!. U.S. Dcpt. Agric.. Forest Seri-icc. Intrrniountain Forest and Range Esperiment Station. Ogden. UT 8410 1. Rcsturch Paper LNT-333. van Jole. M. and VaIlauri. D. 1997. Influence of vegctation changes on forest Ii!.drology and crosion in sniall inountain \i.atershtids. Ceiiiagrtf'. Ln RecIitrcI~cpour L'liigenitrt. de I,'.Agiiculiurc ci de L'Eni'ironnwni. Kimniins. J.P. 1987. Forest Ecolog!.. Xlacniillan Publisliing Co.. Nttv York. S.\'. Krriuse. H.H. 1982. Nitrate fortnation bdore and aftcr clearcutting ot'a nionitort.d Ivatershcd in ct.ntral Sc\\ Brunsoick. Canada. Canadian Joiirnal for Forcst Rcsexch. Langmuir. D. 1997. Aqueous Eni~ironnienialGeocheniistr!.. Prentice-f-Ml Criiiada Inc. Toronto. Canada. MatrikaIli. N.M.. and Richards, K.S. 1996. Estimation of siirface \vater qualit!- cliangcs in response to land use change: Application of the esport coefticient niodel usin2 remotr sensin? and geographical information s!.stern. J. Eniiron. >lanage. 48263- 282. MacGrego:. I4.G. 1994. R&D Report No. 10. Literature on the effccts of eni.ironnient and forests oii lvater qualit) and yield. Can. For. Sm.-. Maritin-ies Region. Nat. Res. Canada. 'Iihan. M.G. 1986. The evaluation and measurement of evaporation. transpiration. interception. and eixpotranspiration. Water Rssources Data Collection. Moffatt. G.R. 1998. Watershed analysis lvith a Geographic lnfomiation Systeni. BScF tliesis. Faculty of Forestry and Environmental Management. Uni\.. of Ne\\- Bruns\vick. Fredericton. Ne\\ Bruns\vick. MoIdan. B. and Cerny. J. (Edirors) 1994. Biogeochemistry of small catchments: A tool for cn\iroiiniental researcli. Scientilic Conmittee on Problenis of the En\~ironinc.nt of die International Couilcil of Scisntific Unions. John N'ilsy and Sons. Chicester. England. Nicolson. J.A.. Foster. N.W.. and Morrison. I.K. 1982. Forest har\.esting effects on \vater quality and nutrient status in the boreal forest. h:Proceedings of the Canadian Hydrology Syniposium '82: H!,drological Process of Foresied Areas. GBS-IZ.C3. Onierink. J.M. Abcrnath!.. A.R.. and iYIalc. L.M. 1981. Strcani nutrisnt Itxds and prnsitiiit! ol'agricultiiral and forest lands to strcarns: Sonie rclationsliips. J. Soil \Vater Conssr\,. 36: 727-23 1. Osborne. L.L. and \i'ile!.. M.J. 1988, Enipirical rclationsliips bct\\wii Innd lise co\.ci- and streani nater qunlity in an agricultural ir.atsrshed. J. Eni.iron. >Ianagc. 16:9-77. Patric. J.H. 1980. Effccts o!'tvood products Iian~eston forest soi1 and nliicr rclatioiis, S. En\.iron. Qual. 9: 73-80 Protvse. T.D.. and Onmianiiey. C.S.L.(Edi~or.~), 1990. Nonliern I-l!,drolog!. - Canadian Perspecti\.es. En!.. Can. National i-Iydrologj. Research Institutc Science Rcp. 90.1. Inland \\?aters Directorate Conservation and Protection. Smith. P.G.R.. Gloosclit.nko. V.. and Hagen. D.A. 1991 .Coastal ivetlands of three Canadian Great Lakcs: inventor!.. current consen.ation initi;iii\ei. and patterns 01' \.ariation. Can. J. Fish. Aquat. Sci. 48: 158 1-1 594. Stade!. B.. and Arp. P.;\. 1998. Timbcr I-Iar~citingin Forcstsd U'aicrslieds: Impacts on b'atcr Qualit!. :\ Reviw of' Litcrature. On behalf of tht. Fund! ~1odc.lForest Soi1 and l\;ater Qualit! Task Group, A\ silorfioinnda Forest C\'atersllrd Manageriiriit and Consen.ation Research Centre. Stuinni. \\'.. and Morgan. J.J. 198 1. Aquatic Cheniistr!.: An Introduction En~pliasizing Chernical Equilibria in Naturd Waters. 2" ed. John F. Wilq and Sons. Inc. Toronto. Canada. Stickney. P.L.. S\vifi. L.W.. Jr.. and Sii-ank. W.T. 1994. r\nn&ted bibliograpliy of publications on \vatershed management and ecological studies at Colveeta Hydrologie Laboratory. 1934- 1994. USDA For. Serv.. SE Forest Esperinient Station. Gen. Tech. Rep. SE-30. Symons. P.E.K. 1977. Effects of logging on a forest-stream ecosysteni. Carnaiion Creek .- -. Project Annual Keport. Paciric ~ioiogicaiSrariun. 7 Tufforci. D.L.. hIcKellar. H.N.. and Hussey. J.R. 1998. In-strean~non-point source nutrient prediction ivith land-use prosimit!' and seasonality. J. Eni.irori. Quai. 17:lOO-111. \Venger. K.F. (ediror) 1984. Forestr! Handbook. John F. It'iley & Sons. Inc. Toronto. Canada. \\'hite. J.B.. and Krause. H.H. 1993. Tlis inipacr of forcst managcmrnt practices on \\atcr qiiality and the establishment and management of protecti1.s biif'f'rr zoncs. :\ rt.\+.x ot'[itcratiire. Coopcration Agrcernsnt on Forest Dt.idopnit.nt. l'anni. S. 1995. tl~~drogeoclien~icalasscssnient of aarcr in torestcd \\~aterslicJsrit Kejinikujik National Park: Discharse raies. chrniical coniposition. and ion Iluscs. MScF Tliesis. Faculty of t-orestry and Enviroiimental \~lnnagt.nit.nt.Cini\.. «t'Sc\\. Bniiisi\.ic k. hiap creation procedure: taken from Moffrttt (1998). 1. ACQLIRE AND COMPILE DATA The first stage in this project ini.ol\.ed the acqiiisition and cunipilrition ot' ~qetation(FOREST1 lalw of the NBLIB library. SBDNRE 1997~).soi1 (SOIL h~w-of the SBLIB library. KBDNRE I997h), waiercourse (LISE Iayr ot'tlit. NBLiB librar),. SBDNRE 1997~).aiid terrain elt'\'rition (NBDSRE 1997ri)claia. dl in tltc' forin ot'GIS "thsmcs" hrtlw are3 of intt'rèst. l'li~st'data SCIL tram I~C XBLIB database. wrs impontld into the ARC ( Einhmental S! stèiiis Rcisr.arcli Institutt 199710 spatial datribasc f'omiat. and tlit. indi~.idualniaptiles nwc. joinecl togeth into a siilgis largc riiap for cach thenit.. 2. DELISEXTE \!'ATEUSHEDS Stst. usine the \j.atcrcourss tliemc in ARC!VIEbr (En\-ironnicntd Slstc~ns Resrinrcli Institutt. 1997h). each natershed uns rou$ly detined based on the strcani net~~ork(Le., ihe border is dra\vii ben\een streams tloning into tlic monitoring station. and those flou in2 awa).). uith the Stream monitoring station as the origin of the \tatershed. This boundary ivas then made more accurate by o\wlaying the terrain elevarion theme (as contour lines) on top of the natercourse theme and redrawing rhe natershed boundary to coincide 1% ith the topographically definable \vater diiides. The watershcd boundary ivas establislied as a single poly,"011 on a separate thcnie. or map laj.er. 3. ESTR4CT i1;ATERSHED DATA FROM THEklES The nc\vly crsated liatershed-shaped polygon tix theii i,uo..wc~(etl!\.;th the soil and \tgetation thsmes. That is. an!. area in the soi1 and vegetation thsmes thrit la! witliin (lis arca coi*eredby the umxsiied polygon \sas isolaicd froni 1hc siirrounding arta. TIILIS.the attribiite tables for the soi1 and \qstarion themes noiv coiwed onIl. tlie tvatcrs1ir.d nren. From thest. rittributc tab1t.s. a u.calth of'intiirtn;iticm ma! be t'ound. L'sii-ig database niriiiagetncnt sofr\\.m (sucli as Parridos, Corcl Corp. 1997) and sprsndslieer sohvm (such as Escèl. Xlicroso fi Corp. I CN6 ). siinln-iar!. information regarding the (oral watershed area. ihc soh~oodand Iilirdwod criinpcisition. and the awrigci soil structure and drainagc clriss across ttic \vwrslicJ ma! bc dwrminsd. III thri Iiirgrr project ofuliicli rliis thcsis is a pari. this information u.ill 1ie used as input fSr a. toresr h).drology modrl. (ForHl-LI. .-\rp alid Yin 1992). ~vliich.\vlleii conlbined ivitli tveathcr data. yields strearii dischargr. and lience dIo\is the coiltxrsion of nutrient and ion concrintrririoris [as coniriirlcd in dit. [vater qualit! data) into tluses o\w time. 4. M.4P WATERSHED DATA .As nt11 as isolating the \vatsrshed fion1 the surrounding area. the GIS platform also allow the natershed characteristics to be displaj.ed grapl~icall!~. printed. or pIotted. By selecting and o\.erla!.ing certain themes. niaps cari be devslopsd to shoiv: 1 j \varerslied boundaries 2) \vatercourses 3 ) topography 4) location and estcnt of sil~~iculturalactii.ities 5) stand types 6) udands. iirban areas. and otlier non-forest land 7) soi1 t>,pr.s and drainage class etc. In addition. the GIS softnare can. from the ele\.ation data. deri\.e slopes anci aspects. \\.hich can be displayed. and ivliiclr 1%il1 be iisetùl for interprtiting the et'ftcts of forestr!. acti\'ities on streani \\.ater qualit!,. J.> STRE.4M CROSSING DELINE.ATION To delinsate stream crossings, strcam or road line features need to be con\wted to polygons by hi!ff>rii~gthem. Tliese pol!.gons can then be itit~w~w~~i nith sach otlier. E\.ery intersection of road and Stream features represçnts a crossing of the strearn b!. a road. although the type of crossing (cul\wt or bridge) is ncit knoivn. Care must be taken Ivheii selecting the tvidth of the buffer around the line feature to bs intersected. For esample. a stream could be bufî'ered and arbitrarily rriven a nidth of ik.e meters on each side of the stream center line. If an' road (as a b line feature) ii.cre to pass ivitliin five meters of the original streani center line. it u.ould bs treated as a Stream crossing regardless of ~vhetheror not it actually crosscd the streatii. Stream buffers should therefore be set to Iess than ont: nieter in \\.idth.