Methods for Estimating Inflow and Infiltration into Sanitary Sewers

Transcription

1 CHAPTER17 Methods for Estmatng Inflow and Infltraton nto Santary Sewers Davd Crawford, Paul Eckley and Edwn Per Excessve nflow and nfltraton durng wet weather perods nto capactyconstraned sewer systems cause santary sewer overflows (SSOs). The two major components of wet weather flow are nflow and nfltraton, and arc the man factors found n santary sewer evaluaton studes (SSES) or nflow/ nfltraton (III) studes. Control and reducton of nflow and nfltraton drectly relates to effectve controls for SSOs. The nteracton and relatve proportons of nflow and nfltraton determne the extent, effectveness and cost of control measures. Usually, control of drect nflow s the frst source pursued, wth the nfltraton component ether lumped nto part of the nflow as an mmedate response, or neglected because of the domnance of peak flow rates nduced by nflow. The peak flow rate, as compared to sustaned elevated flows from nfltraton, s usually the sought-after result n SSES or III studes. Successful and accurate estmates of both ranfall-derved nflow and sustaned flmvs from ranfall-derved nfltraton are therefore the prme determnants ofthe effectveness and cost of the controls. The objectve of ths chapter s to summarze and provde some crtque of C01l\1\0\'\ 't'\0\v -pm)ect\on met\1odo\oges, \)artcu\arly methods that\)red\ct ranfallderved nflo~v and nfltraton or ranfall-derved nflow/ nfltraton (RDH). A summary ofthe most common methodologes s ncluded. Space lmtaton precludes ste by ste comparsons of each technque, even f project objectves (and fundng) allowed such comparsons to be made. Crawford, D., P.L. Eckley and E. Per "Methods for Estmatng Inflow and Infltraton nto Santary Sewers." Journal of Water Management Modelng R do: I /JWMM.R CHI ISSN: (Formerly n New Applcatons n Modelng Urban Water Systems. ISBN: ) 299

2 300 Estmatng Inflow and Infltraton nto Santary Sewers The chapter wll nclude dscusson ofthe approach used n two communtes, one of medum sze (populaton 160,000) and one large (800,000). Both communtes have examples of vared nfrastructure age and condtons, and clmatc dfferences that affect nflow or nfltraton predcton. The two commun.tes have large captal mprovement programs that are sgnt1cantly mpacted by the qualty and accuracy of flow estmates of desgn stonn flows. Desgn flows are estmated for current flow condtons as well as for future basn condtons wth growth projectons and nfrastructure mprovements. Infrastructure mprovements n general and specfc to Salem, Oregon and Honolulu, Hawa are to reduce nflow and nfltraton, normal lte cycle system mantenance, or to enlarge sewer system capacty Santary Sewer Flow Components Any effectve technque for estmatng wet weather nflow or nfltraton must recognze the dfferng sources of water n a se\ver system and be able to ndvdually estmate or combne the components when producng wet weather flow estmates. A typcal segregaton of sewage flow can be made nto the followng components and as shown n Fgure 17.1 [note: 1 mllon gallons per day (mgd) equvalent to cubc meters per second (m 3 /s)] Typcal Flow Component. Flow Mantor. ADS18 Rajn Gage: 002. ~ 0.00 I t I t 0.30 I ~ _ ~ O.50~ ~ 0.60 '" \ ; ,...._... _._-"" '24~' Fgure 11.1 Typca flow components n santary sewers.

3 (ADF): Base th)m communty's domestc, commercal and ndustral customers, the everyday sewerage generated by the communty. Ths flow usually has a dumal wth a normal peak occurrng n the mornng wake-up tme, sometmes another peak occurs n the afternoon and a low flow durng the nght and early mornng hours. Industral and commercal components would follow workday patterns. The dfference between the montor flow and ADF at the start of Fgure 17.1 s due to resdua! nfltraton from prevous ranfall. Groundv/ater: Sewer flow usually assocated wth seasonal changes n base nfltraton to the system but not as a result of mmedate wet perods. long cyclc change n ground water levels due to seasonal ranfall patterns has the greatest nfluence on ground water flow. Ppe condton and relatve elevaton between the ppes and ground water level also affect ground water flow. Inflow: Usually an mmedate and notceable rse n sewer flow as a drect response to ranfall. There s usually a short tme Jag between tme of ranfall and observed sharp rse n flow response. Inflow s dependent upon antecedent condtons, the condton of the sewer system partcularly lateral connectons and the number of drect hydraulc connectons. o Infltraton: Response to ranfall. Typcally a slower response to ranfall that can buld wth tme as ranfall contnues and may last for several days after ranfall stops. Infltraton s dependent upon antecedent condtons, ppe condtons and sol characterstcs. Common methods for estmatng the components of sewer system flow usually perform the followng determnatons and consder the varous effects mentoned above: Determnatons of dry weather flow and ground water nfltraton. Detennnaton of flow rates for nflow and nfltraton. Determnaton of hydrograph shape or constant peak rates. Incluson of effects of antecedent condtons. Evaluaton of possble changes to nflow and nfltraton components due to remedaton projects Evaluaton of Flow Methodologes Many methods have been used to develop flow estmates nto sewer systems. It s common to develop extensve data and processes to estmate dry weather flows and dumal patterns and only cursory data and sngle value estmates of wet weather 11ow. Data on basn areas, and populaton are collected and to accurate estmates of daly

4 302 Estmatng Inflow and Infltraton nto Santary Sewers flow rates. Durnal patterns or daly peakng factors are also developed and appled to dry weather flow to produce a peak dry weather flow rate. On top of ths flow s usually appled a constant peak wet weather flow reated to a basn's sze, total populaton or populaton equvalent. The relatve levels of effort expended f! developng dry weather flows versus \-vet flows are often n reverse tlyotlort.on to tne 'PeaK fow genemteu b'j t\\e t\\lcl c\m\\1\me\\\~.1'n\'b!,)n.~\\ results n conservatve peak flow rates, uncertanty or total 110ws and greatly affects the szng of sewers and SSO control Obvously t s more dffcult to develop wet weather flows that are a result of spatal and tme varyng ranfall over the sewered basn. The followng therefore provdes a summary of common methods for estmatng wet weather flow n sewerage systems Constant Unt Rate Components ofrdu can be estmated as a constant unt rate based on se\vered basn (sewershed) characterstcs. Unt rates are dependent on many varables and often vary sgnfcantly from sewershed to sewershed. Most communtes however have peak nflow nfltraton rates ncorporated desgn manuals. The desgn manual specfes the unt flow rate, usually as a rate per acre,!()r new or exstng development. Some common assumptons for constant rates are the projecton of peak f10w rates recorded at the wastewater treatment plant to other parts of the system based on basn area or length and dameter n the basn. RDn from large areas should be based on ndependent varables such as sewershed area (gallons per acre), land use (gallons per acre of specfc land use), populaton (gallons per capta), and ppe length (gallons per foot of ppe). Small areas can be fui1her refned such that the ndependent varables nclude characterstcs related to system defects (leaky manholes, poor ppe condtons, etc.) and to other characterstcs such as ppe materal and age, roof dran connectons, back-fll materal, and constructon practces Percentage of Ranfall Volume (R-Value) The rato, n percent, between the volume of wet weather flow at a montorng locaton and the ranfall volume that falls on the sewershed area served by the montor s defned as the R-value. To compute the R-value, average base flow and ground water flow components are frst determned by analyzng metered data for extended dry perods. Wet-weather RDU s then detennned by subtrac:tng the average base flow and ground water components from metered data for typcal duraton wet perods. The volume ofrdh for sgnfcant (typcally events wth greater one nch - 25 mm - s data and dvded storm.s. the ranfall volume to

5 I? 2 E.. 'aluaton lvfethodologes 303 Addtonal analyss s usually carred out to determne approxmate hydrograph shape for the tme ncrement ofthe ranfall or desgn storm selected. A trangular hydrograph shape s typcally used. The hydro graph shape determnes the tme to peak nflow and the tme for flows to recede or the recesson of flow atter ranfall ends. Each tme perod trangular hydrograph s supermposed wth the other hydrographs to produce the desgn storm hydrograph for the total storm event duraton. Under deal condtons the R-values would measure the response of the sewershed to ranfall events and are therefore dependent upon antecedent condtons. Both the amounts of nflow and nfltraton wll be affected by the condton of antecedent sol mosture. Ranfall varaton across the basn and uncelantes n measurng flow and ran are two examples that make condtons Jess than deal. The assumpton of dry weather and ground water flows can also greatly affect estmaton of the R-value. Fgure 17.2 shows a seres of storm events. Often seasonal envelope curves are developed to represent wet or dry antecedent condtons. Fgure 17.2 llustrates wet and dry envelope curves drawn to enclose the hgh and low extremes. For desgn purposes an upper bound or envelope curve for R-value s determned based on a plot of all recorded or selected R-values. The ranfall desgn storm volume and duraton s determned from ranfall frequency analyss. The correspondng desgn storm flow volume s selected from the upper bound R-value for the desgn ranfall amount. The desgn storm volume s combned wth the assumed hydrograph shape to determne the wet weather nflow l - -"-!..!IJYCoMhon EnvelfJPel3~ l.... ) I 1--.._._ , ~ _ _...J Ranfall + Snowmelt Volume {lncms} Fgure 17.2 'R' Factor determnaton... ; I

6 304 Estmatng Inflow and Infltraton nto Santary Sewers hydrograph for that pont n the system. The total hydrograph s the sum of the wet weather hydrograph, the usually assumed constant ground water flow and the durnal dry weather flow Predctve Equatons Based on Ranfall and Flow Regresson The ranfall and flow multple regresson method uses observed ranfall and flow to derve a relatonshp between ranfall and ROIL Ths relatonshp s then used to estmate ROn flows for other ranfall perods of nterest or for a desgn stann wth gven volume and duraton. Because ranfall response wll vary wth antecedent condtons, t s necessary to develop separate equatons to represent summer and wnter condtons. The equatons can be adjusted to ft both the nflow peak and recesson lmbs ofthe sewer flow hydrograph. The general form of the equaton relates tme perod ranfall to the wet weather response, such a..<;: where: wet weather response n cfs or mgd seres of coeffcents produced from regresson analyss, and ranfall values correspondng to the tme perods selected durng the regresson analyss. A graph showng the 'creaton' oftheregresson equatons s gven n Fgure Fgure 17.3 shows the durnal pattern and daly flow pattern. Ths pattern oftlow s selected from montor data (durng dry weather) and appled consstently across the tme perod. The wet weather flow s added to the assumed dry weather flow for each tme step (usually one hour) to reconsttute the total wet weather flow at the montor ste. The regresson equaton s gven at the bottom of Fgure 17.3 and shows the coeffcents and tme ntervals used to determne the ranfall to wet weather response relatonshp. For ths montor locaton, tme ntervals rangng from 1 hour to 168 hours have been used wth 360 hours not used (hence the zero coeffcent). The process developed to create the relaton w shps can be easly modfed to change the tme ntervals or drop tme perods from the regresson. Good data s obvously a prerequste for a successful regresson. Fgure 17.4 shows the extenson of the regresson analyss to other tme perods. Average dry weather flow pattern s agan assumed to be consstent across the tme perod wth wet weather response estmated from ranfall data appled through the regresson equaton for the montor ste (Cra\vford Engneer- Assocates, 1997).

7 30S r ( fu!o 7, ::::L-----~ ~ ~----!---.. ft~~ ~~ Equ~w.:m C$~~1't flow M""IQr. AOB1S Ran Gogo: RG2 -'!f'"""""-"---' J: f.ZBJ94 12Jl(Y94 Equatun: RDB n O,SO", h' -} 2.03"'3 hj' "0 hr *12 hr "24 hr ~ 1.7$ 48 hr -+ O.82*9G hr + O.13~166 hr "360 ht Fgure 17.3 Regresson tlow compared to montored flow r Q,OO I f 0,10 10,20 I I -1 I 0.40 _ I ~.l 0,50' ' I :! "I 060~... -I 0,70 "',1 t Regresson Equmon Calbraton fow Montor: AOS18 Ran Gage: RG2 ':: rl-r~'l1nr'it--'-' , -'f l"' 0.00 '6".. '" ~ ~ G -~ , _j_ O.lO OAO.. ~ 0.50 :? J!,~ 0.60 It: ;t~:c 1.00 ~ ~""-. -"-----""---~--~--~------'''''-'''---'--~----~-----'.~ ~ ~.l,do 12/ HS4 12/24.'94 12/31/94 EqtltMlOrr RDU '" :: hr + 2.U3<3 hr hr "12 hr *24 hr ~4S hr... O.f2~!#"0 hr rof 1- O,()OV36Q hr Fgure 17.4 Applcaton of ranfall- wet weather flow to other tme perods.

8 306 Estmatng Inflow and Infltraton nto Santary Se'wers The uncertanty n applyng a pont ranfall record to an area and the accuracy of the montor data all tend to cause the engneer to select the tme perods that produce the' best ft' curve to the observed data. I n other words vsual judgements rather that rgorous statstcal applcaton are appled. The sholter duraton tme ntervals of the regresson help the model express the amount of mmedate nflow whhe the \ongert.mcs her) model the nf\\ttatkm that can occur when ranfall stops. The former selected to match peak flows and the latter the recesson of flow. Other examples of regresson applcatons n waste\vater analyss can be found n Berthouex and Box (1996) Other technques Obvously there are many other methods and technques avalable to produce relable and approprate wet weather flow hydrographs. Several other technques are summarzed below. The nature of the study area and the objectves of the study should be consdered n applyng approxmate or other methods, partcularly methods that are not verfed aganst sewer flow Percentage a/streamflow: 'Streamflow can be a relable predctor of RDII because the combnatons of hydrologc condtons that ncrease streamflow are smlar to those that mpact RDH enterng a sewer system. A relatonshp s developed between sewer flow montorng data and streamflow data. The relatonshp s used to provde flow estmates n non-montored basns but wth adjacent streamflow data. In applyng ths relatonshp t s assumed that the sewer system condtons where the relatonshp s beng appled consstent wth condtons n the montored sewershed areas. Unt Hydrograph: Unt hydrograph theory can be appled to sewer system montor data just as n other hydrologc analyss. The unt hydrograph method assumes that RDII responds to ranfall volume and duraton n the same manner as stormwater runoff and that the shape of an RDH hydro graph s a functon of basn characterstcs. Basc assumptons of the method are: that the tme duraton ofrdh hydrographs attrbutable to ranfall of a specfc duraton are constant and the peak flow s proportonal to the volume of ranfall. Based on these assumptons, unt hydro graphs for RDII can be developed and calbrated aganst montorng data. Several unt hydrographs may be necessary to represent the separate components contrbutng to the RDII hydrograph (nflow and nfltraton) and adequatejy represent the hydrograph shape. The resultng unt hydrograph can be scaled n proporton to a ranfall of desred ntensty and duraton to produce an RDH response hydrograph.

9 17.3 Technques used n Salem and Cty and County (?lhonoulu 307 \I Probablstc: The probablstc method s a frequency analyss peak ROIl flows and s smlar to classcal hydrologc frequency analyss for extreme events such as floods or peak ranfall events. The method can be appled to ether sewer flow rates or event volumes. To apply the method, the peak value (rate or volume) s dentfed for each month n a perod of record that covers a representatve perod of wet and dry seasons. A statstcal analyss s performed to determne recurrence ntervals for each of these flows. The peak flows are ranked and plotted on log-probablty paper. Values for varous recurrence ntervals can be determned f'om the graph. Unlke some other methods that strve to derve RDn as some functon of ranfall, the product of a probablstc analyss s a relatonshp of sewer flow to recurrence nterval. Greaterdscusson ofths (and the many other technques mentoned heren) would volate the space allotment for ths chapter ROll as a part of Commercal Hydraulc Software There are numerous hydrology/hydraulcs software packages that are wdely used n the ndustry. These nclude XPSWMM, Mouse, Hydro Works and Hydra. All ofthe packages nclude methods (wth varous levels of detal and sophstcaton) for generatng dry weather flows and wet weather nflow and nfltraton. Detaled revew oftne methods ofthe commercal packages s beyond the scope of ths chapter. In most cases the packages rely on standard ranfall runoff prncples that consder hydraulcally connected area, nfltraton and ground water to sewer flow nteractons to produce wet weather!low. Dry weather flows are estmated through assumptons on unt flow rates and populaton estmated and equvalents. Most also allow mport of externally determned flows. Input of externally generated flows to hydraulc model systems s a common practce n SSO and SSES studes. Combned sewer overflow studes usually nclude watershed evaluaton to determne stomlwater runoff (such as wth the S\VMM RUNOFF block). In many cases one ofthe technques outlned above s used to nput the contrbuton f'om santary or separated areas that contrbute flow to a combned sewer system Technques used Honolulu Salem and Cty and County of The dscusson of the technques and some of the observaton of use and results obtaned from applyng the flow estmates to the two systems can only provde a cursory overvew and some salent observatons. Much work has been

10 308!.stmatng h?flow and If?ltraton nto Santary Servers performed n collectng and analyzng data, evaluatng alternatve flow methodologes, and assessng the flow-related mpacts to the two collecton systems of the Cty of Salem, Oregon (Salem) and the Cty and County of Honolulu, Hawa (CCH). Salem has a current populaton of about 160,000 and CCH serves a total populaton 1,000,000. The ntent ofthe sttldes should be consdered when applyng ~md as~essng approprate now estmaton methods. No sngle method s perfect; f the flo\,1 methodology s selected wth the objectves ofthe study clearly n mnd and the degree of conservatsm nherent n the method understood then results produced wll usually be acceptable. The dscusson gven below s not a crtque of the work performed by the many people and companes that have been nvolved wth the varous studes. The studes for Salem and CCH contan many common elements. These nclude: Recognton of the need to montor datu throughout the system. The montor data conssts of both permanent montor locatons, for long term evauaton of the collecton system performance and temporary gages that are used to determne and solate basn t1mvs. The latter gages move around the system to 'fnd' the leakest sub-basns and to assess the success of remedaton projects. Flows throughout the systems can be extreme. Some basns have peak wet weather flow to average day flow ratos of Many basns however have ratos n the expected range 00 to 6. Sol type, hydrology, system condton, constructon practce and age of ppe are factors determnng the wet weather response of the systems. Even n the relatve small area of Salem there are observable dfferences between sewered basns that can be factored by some physcal parameter such as age of ppe. In CCH the ranfall patterns playa substantal role n determnng flow rates n the many basns ofhe collecton system. Both systems are subject to surchargng and floodng or overflows. The regulatory pressures to elmnate, control and reduce overflows are hgh n both communtes for many reasons. Each system has as a startng pont a standard storm to control. In Salem the 1 n 5-year, 24-hour stolw durng wnter months s the controllng event. In CCH a range of events from 1 n 2-yearto 1 n 1 O-year 6 hour storms are beng consdered. For Salem extensve use of the regresson method was used to estmate flows from basns to the collecton system. The flow estmates were used as nput to a detaled hydraulc model of the system. More detals can be found n the reports and appendces of the SSES study and Master Plan (CH2M Hll, 1994, 1996).

11 17.3 Technques used n Salem and Cty and County of Honolulu 309 In CCH the prmary studes are for sewer rehabltaton and III mnmzaton (CCH, Fukunaga and Assocates, I 994a and b, 1997). The' R' F actor method was used n CCH wth dry weather flows computed usng the WIMS (Wastewater Informaton Management System) (CH2M Hll, 1995). The effect of the flows to the collecton system was smulated usng a hydraulc model developed from data contaned n WIMS Selectec observatons anc use of the flow methodologes Estmaton offutureflows from exstng and new development A sgnfcant concern n many SSO and SSES studes s the estmaton offuture condtons. Pr~ecton of populaton growth and defntons of urban growth boundares often provde the framework for determnng future base and peak wet weather flow rates. Base flow rates can be obtaned from overlay of traffc analyss zone data or other basn data wth sewershed areas n elaborate GIS systems (see Crawford and Lmtaco, 1996). Often estmates offuture peak flow f'om newly developed or developng areas rely on a smple assumpton of a constant unt flow ted to estmated area or populaton. In Salem, an approach was adopted to refne these future rates, based upon observed montor data and projected rates for the 5-year 24-hour storm event usng the regresson equaton developed for the montor locatons. Fgure 17.5 shows the relatonshp found between the peak rate per acre for the 5-year storm and the average age of ppe n the basn. The varaton n unt flow rate wth age s apparent. Ths relatonshp quckly dspersed the dea of usng a sngle unt flow rate for new areas, future basns that would develop n the next ten years but would be 20 years old at the 30 year plannng horzon, or basns that have populaton growth but no ncrease n area. Based on the t1ndngs of ths analyss, Salem adopted 1,SOO gallons/acre/day (glald) for 10 year old basns, 3,OOOgad for 20 year old basns and 6,OOOg/ad for 30 year old basns. Ths approach allowed for the expected deteroraton of the collecton system as basns aged and provded a better tool to plan new sewers and expansons to treatment facltes and pump statons (CH2M Hll, 1994, 1996). Many basns hovvcver had reductons n basn flow rates due to rehabltaton projects. The regresson approach also provded a means of determnng hydrograph sha\1e far future basns. the wet weather flow for new basns were based upon the hydrographs and!lxmd from montor data newer basns of the exstng collecton system. Adjustments to flow rates were scaled usng basn area. Regresson analyss lmtatons A major drawback of the regresson approach s the adequate representaton of the seasonal ranfall-nflow/nfltraton process n a santary sewer system. Adequate and representatve ranfall and flow data are pre-requste for ths

12 310 Estmatng hflow and Irfltraton nto Santary Sewers Peak Hour). RDIl r'le"w Wle p-..'r B.lsIn CompJred to t:!) of Su em ASSI.Il-llcJ Vatut;S 5 YCJ:lf DCS~lf Storm Fmm KCl!(c;;-~on Eftwtlu;:!; 9,{l{JU r--'~-' '.-'-----'-".""""""-""-"---"'..._.._-_.., 1,UOO.CwkCrxk.. FoJI~1~f:t;?{('b Imj 6,noo 5,H! J 4,{)W] 3,000. 2,WO II WYcl}rOId B;!sm 1,000.. W..!uCreek ll 2:t: Y CM Old BMta Brush Cdkgc W Fgure 17.5 Estmaton of unt flow rates and average ppe age n a basn. 30,00, I 25,00 T r Ragreuon Equaton CaJbr.. ton Flow Montor: ADS18 Ran Gage: RG2!f---rrlll"~-1 ::....,. Monjtor Flow l BaseAow! _Regre'3son!,..:..._~I l020 I.j. 0.3(;! ,, 0.60 c::: ~. Wl21~ Fgure 17.6 Chart.l.m,.; " one of the lmtatuns

13 17.3 Technques used n Salem and Cty and County of Honolulu 311 method. The processes nvolved are very dependent upon antecedent condtons. In Salem, the smulaton of flow proved adequate for wnter condtons and was duplcated between wnters (December through March) over several years. However smulaton of early fall and summer flows was nadequate as shown by Fgure Fgure 17.6 shows the applcaton of the wnter regresson equaton to a severe storm event (close to a 25-year storm) that occurred n late October after a prolonged dry perod. The predcted flow response of the system greatly exceeds the actual flow measured. As the wnter progressed, the computed flow response became more n-lne wth recorded data. Dry weather flow however was not much dfferent than that used n the regresson analyss. A separate seres of regresson should be developed to represent the seasonal nature of the ranfall~ flow processes. 'R' Factor AnalysS Lmtatons The ranfall-flow processes n santary sewer systems s lmted to the ablty of the flow connectons, leaky manholes, damaged laterals and lnes to take n water. An upper lmt to peak flow s therefore expected. Infltraton may however contnue as groundwater and saturated ground condtons are "draned". To smply extrapolate response percentages to hgher values for greater storm events would lead to a smlar trap as that llustrated n the regresson response gven above. Our colleagues at Fukunaga & Assocates have modfed the 'R' factor response by lmtng the slope of the envelope curve of the analyss (Fgure 17.7) (Fukunaga & Assocates, 1997). 06 Example of 'R' FQctor Relatonshp : I ; , ~_ :J:\:.~ ;? J o.~;.l-'--~---~o '0 SoLJ.fCO: FUV;3,"Iff1fjfJ & As.soac!as Fgure 17.7 'R' Factor relatonshp n Honolulu.

14 312 Astmatng Injlow and Infltraton nto SantGlY Sewers The l-year 6~hour event and other desgn events (see Fgure 17.7), are derved from statstcal analyss oflong-term ranfall records, selecton ofhoudy ranfall patterns for the events, and applcaton ofthe 'R' factor to determne the wet weather RDn volume ufthe event. For vsualzaton of the 'R' factor as a n 17.7 are same gahons or cubc rneters wth appcaton of approprate unt converson), over the basn s converted to volume by multplyng the ranah estmate by the sewer basn area. The curvature ofthe envelope curve has lttle effect on the I-year 6-hour '.vet weather nflow estmated volume; due prmarly to the recordng of several events close n magntl1ce to the I-year 6-hour storm. The effect on less storms s more pronounced as shown n Fgure The estmated 1 hour wet weather nflow volume would be reduced by 25%. The lmts placed on the envelope slope was determned from a thorough knowledge of the coliecton system response to ranfall and detaled knowledge of the over 120 gages n the system. Contnued collecton of montor data, partcularly hgh flow events at crtcal locatons wll enhance the relatonshps developed. The effect on the szng of collecton system facltes s gven n Table 17.1 for the larger pump statons and four ofcch's large treatmentpjants. Table 17.1 gves the estmated capacty of key facltes for the desgn ston11s smulated usng the estmated nflows generated l'om the dfferent desgn storms and routng of flows through 11 hydraulc model of the collecton systems. Table 17.1 Large pumpng staton and treatment plant capacty projectons, MGD. Faclty Ex'stng Current Condtons (1995) Future Condtons (2020) ----'~-~"--Cpacty 2-year 5-year ----ro:year 2-year 5-year W-year stonn stann storm storm storm stom1 SlldISlruld WWTP " Ala Moana Beach Walk Kahala ! 19.5 Awa Street Hrut Street Honoulul WWTP Pearl Cty Wamula Kalua RWWTP l :1.6 Kaneohe Vv'WTP KaHuaRolld (1.& l3.0 J4.4 ].2 ~ Ka!hm Heghts \7.8 15,1 17,! J8.0

15 J7.4 Conclusons 313 For all facltes the analyss shows a substantal expanson oftreatment and pumpng capacty s requred to can)' flow and control SSOs. Tabe 17.1 does not reflectthe extensve redevelopment ofthe collecton system feedng the facltes. The hydraulc model (usng TRANSPORT) for the collecton systems n Oahu allow forreszng of all ppes to accommodate peak flows. Ths latter assumpton may be modfed n the future to reduce flows by ncreasng n-lne or other storage effects of the collecton system. Other hydraulc model consderatons are the effects of condut surchargng and tme ofranfall events wth tme attenuaton wthn the collecton system. The routng of flow usng TRANSPORT or EXTRAN wll affect the szng of facltes Conclusons For the successful applcaton of the technques hghlghted n ths (:hapter and used n Salem and the Cty and County of Honolulu the followng tems are requred: The technques need good basn physcal and relatvely extensve ranfall and flow montorng data. Basn physcal data nclude area, populaton and employment, ppe and manhole data, and condton assessments. Flow and ranfall data collected should represent the system condtons. After these data are used to produce the ntal system flow characterzaton, other more targeted data can be collected n basns wth hgher RDH flow rates. Montorng at permanent stes could be undertaken to gage the performance of RDII reducton programs or the growh n flow n the system. Analyss of montor data and sewer system responses are mportant precursors to successful sewer system modelng. Accurate defnton of the sewer basn - knowng how and what ppes are connected s essental. Overflows and nterconnectons between basns can greatly change flow results and predctons. Interactons n the sewer routng model ncludng ppe surcharge and flow restrctons need to be understood. Flow montor data can provde ndcatons of surcharge and bypassng of 11ows. Floodng overt1ow reports should always be consdered n desgnng montorng programs, n buldng the system or basn hydraulc models. The technque seected should allow adequate smulaton of the regulatory requrements such as gven desgn storms and response to ranfall events outsde regulatory storms. Usual regulatory events are for a sngle duraton (such as a 5-year, 24-hour event) whereas SSOs may occur due to longer duraton but relatvely low ranfall

16 314 Estmatng Inflow and Infltraton nto Santary Sewers events. The Salem and CCH technques provde a method to evaluate longer duraton events and the relatve mportance of nflow and nfltraton n the collecton system. The technques provde a method to estmate a complex physcal ~ Q'""'"'+''"'~' betv.;een ranfall (both seasonal and event based), basn chmactcrstcs, co\\ectan system constrants and cond\tons, and the routng of flows and nteractons wthn the svstem and at kev hydraulc control ponts. J "' In applyng the flow estmaton technques t s necessary to determne the accuracy n the flow estmates and the many flow nteractons. The mpact on master plans, Ill Mnmzaton Plans, and SSO control should be carefully weghed. The results obtaned should be weghed aganst the overall objectves ofthe study and the engneerng solutons derved through careful applcaton ofthe flo\v estmaton technques and other components, such as collecton system modelng, used n selectng engneerng solutons. Acknowledgments The materals presented heren are based on the work of the prmary author and many others. The materal draws upon the dscussons, reports and techncal memorandums produced for studes for the Cty of Salem and the Cty and County ofhonolulu. The sponsorshp and nput ofthe staffs ofthe Cty of Salem and the Cty and County of Honolulu, especally Paul Eckley and Ed Per, s apprecated. The studes utlzed n ths chapter nclude work performed by the prmary author whle at CH2M Hll and the staff of Fukunaga & Assocates. The efforts and good work of staf at Fukunaga & Assocates and CH2M Hll are acknowledged. References CH2M Hll, July SSES/Master Plan FY Report. Portland, Oregon. 80pp. CH2M Hll, August Wastewater Informaton Management System, Phase III and IV-Flow Modelng and Sewer Connecton Applcaton. Honolulu, Hawa. 350pp. CH2M Hll, August Fnal Draft. Salem Wastewater f\.1anagement Master Plan. Portland, Oregon. 45pp. Crawford Engneerng Assocates, December Techncal Approach for Modelng Shoulder Sca,;on n the Salem Crawford, D. and F. Lmtaco "Wastewater Informaton Management System: Flow Modelng and Sewer Connecton Permt Applcatons." Journal of Water Management Modelng Rl do: /JWMM.Rl95-22.

17 Berthouex, P.M. and Box, G.E Tme Seres Models for Forecastng Wastewater Treatment Plant Perfmmance. Water Research, 30(8): Fukunaga & Assocates, August Interm Sewer Rehabltaton and Infltraton & Inflow Mnmzaton Plan- Interm Basn Reports, Sand Island. Honolulu, Hawa, 350pp. Fukunaga & Assocates, August Techncal Memorandum - Flow Projecton Analyss.HonoJulu, Hawa. 30pp. Fukunaga & Assocates, October Sewer Rehabltaton and Infltraton & Inflow Mnmzaton Plan- Force Majeure Report. Honolulu, Hawa, 50pp. 315

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