Hydro-environmental modelling for bathing water compliance of an estuarine basin

Water Research 36 (2002) 1854–1868

Hydro-environmental modelling for bathingwater compliance of an estuarine basin

S.M. Kashefipour, B. Lin, E. Harris, R.A. Falconer*

Environmental Water Management Research Centre, Cardiff School of Engineering, University of Wales, P.O. Box 925, Cardiff,

Wales CF24 0YF, UK

Received 14 February 2001; received in revised form 8 August 2001; accepted 27 August 2001

Abstract

In recent years, considerable investment has been committed to sewerage infrastructure and new sewage treatmentplants in the catchment surrounding an estuarine basin along the north-west coast of England. Although this capitalinvestment has resulted in a marked reduction in the input of bacterial loads, relatively high counts of faecal indicatororganisms are still being encountered in the coastal receiving waters, and the local bathing waters continue to fail on

occasions to comply with the European Community (EC) Bathing Water Directive (1976) mandatory standards. Detailsare given herein of a comprehensive modelling study aimed at quantifying the impact of various bacterial inputs into theestuary and surrounding coastal waters on the bathing water quality. The model domain includes the coastal area and

the entire estuary (namely the Ribble) up to the tidal limits of its tributaries. Faecal coliforms have been used as themain water quality indicator organisms. The numerical model developed for this study combines a depth integratedtwo-dimensional coastal model and a cross-sectionally integrated one-dimensional river model, and is capable of

predicting water surface elevations, velocity fields and faecal coliform concentration distributions across the entiremodel domain. The hydrodynamic model was calibrated using water level and velocity measurements from threesurveys and then validated against measured data from three other surveys. In order to predict the faecal coliform

concentration distributions, variable faecal coliform decay rates were used, i.e. different values of decay rates wereapplied to the coastal and riverine waters, for day- and nighttime, and for wet and dry weather conditions. Themaximum and minimum decay rates used were 2.32/day and 0.71/day for the dry and wet weather surveys, respectively.The model was then applied to (i) assess the impact of previous discharge strategies and investigate the effectiveness of

future capital investment works and (ii) predict the impact of a range of strategic options, including: the effects ofadding UV treatment, constructing storm water storage tanks and incorporating various combined sewer overflow(CSO) discharge scenarios for different weather conditions. r 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Hydroinformatics; Water quality; Faecal coliform; Decay rates; Estuaries; Bathing waters

1. Introduction

The Ribble Estuary discharges to the north-west coast

of England, in Lancashire. At the mouth of the estuarythere are two well-known seaside resorts, Lytham StAnnes and Southport, both with designated EC bathing

waters. The Fylde Coast, which is bounded between

Fleetwood in the north and the Ribble Estuary in thesouth, includes Blackpool, one of the most famousbeaches in England for tourism, with an average of more

than 17 million visitors per annum. The area has fourmain centres of population, Blackpool and Lytham StAnnes to the north of the Ribble Estuary, Southport to

the south of the estuary, and Preston, which is inlandand straddles the river Ribble at the tidal limit (seeFig. 1).

In order to enhance the bathing water quality alongthe Fylde coast, a major civil engineering investment

*Corresponding author. Fax: +44-(0)-29-2087-4597.

E-mail address: [email protected] (R.A. Falconer).

0043-1354/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved.

PII: S 0 0 4 3 - 1 3 5 4 ( 0 1 ) 0 0 3 9 6 - 7

programme has been undertaken in recent years toreduce the bacterial input to the estuary. About d600

million has been invested over the past ten years in newsewerage works and treatment plants along the Fyldecoast and in the Ribble Estuary. Examples include:upgrading the wastewater treatment works (WwTW) at

Clifton Marsh from primary treatment to include UVdisinfection; reducing storm water discharges from thewastewater network by constructing 260,000 m3 of

additional storage.Although the reduction in input bacterial loads has

resulted in a marked decrease in the concentration of

bacterial indicators in the coastal receiving waters,occasional elevated faecal coliform (FC) counts havestill been measured and, subsequently, the bathingwaters have failed to comply with the EC mandatory

water quality standards (see [1]). These failures havebecome one of the major threats to the local touristindustry. More recent field measuring studies under-

taken by the UK Environment Agency (EA) and NorthWest Water Ltd. have indicated a link between theindicator concentrations measured at various sources

and at the compliance points, but these studies havebeen unable to provide robust quantitative data andthereby ascertain the impact of capital improvement

schemes on the bathing water quality.Details are given herein of a comprehensive hydro-

environmental modelling study of the Fylde Coast andRibble Estuary, aimed at quantifying the impact of

various bacterial sources on the receiving coastal waters.

The model domain comprises a range of topographicand bathymetric features, varying from the wide-open

sea to narrow river channels. Therefore, a hydro-envi-ronmental model, which dynamically links a one-dimensional (1-D) and a two-dimensional (2-D) model,has been developed especially for this project. The

integrated model is capable of predicting the watersurface elevations, velocity fields and water qualityindicator distributions along the entire modelling domain.

FCs have been used as the main water qualityindicator organisms in this study. A detailed analysisof the flow rates and FC concentrations, as measured at

various input sources, has been undertaken and has ledto a better understanding of the distribution of thebacterial loads. The decay rate of FCs controls the fateof these micro-organisms and is therefore one of the key

factors controlling the accuracy of the model predic-tions. To achieve more accurate FC concentrationpredictions, different FC decay rates were applied for

the coastal and riverine waters, taking into account day-and nighttime conditions and wet and dry weather flows.The model was then applied to predict the impact of

various coastal investment programmes on the receivingbathing water quality.

2. Theoretical background of hydro-environmental model

The mathematical models used in this project includea 1-D model, namely FASTER (Flow And Solute

Nomenclature

A cross-section area of flow

C de Chezy resistance coefficientDc cross-sectional average longitudinal

dispersion coefficient

Dxx;Dxy depth-averaged longitudinal dispersionand turbulent diffusion coefficient in thex direction

Dyy;Dyx depth-averaged longitudinal dispersionand turbulent diffusion coefficient in they direction

f Coriolis parameter

g acceleration due to gravityH depth of waterk coliform decay rate

qL lateral inflow or outflowQ dischargeR hydraulic radius

S cross-sectional average solute or sus-pended sediment concentration

SL solute or sediment concentration of lateralinflow or outflow

SS cross-sectional average source or sinkterm

T90 a time during which the original organism

population would reduce by 90%U depth-averaged velocity in the x directionV depth-averaged velocity in the y direction

Z water surface elevationb momentum correction factor

%e depth-averaged eddy viscosity

f depth-averaged solute concentrationjd cross-sectional- (1-D) or depth-averaged

(2-D) decay or growth ratejK depth-averaged total kinetic transforma-

tion rate for solutejS depth-averaged source or sink solute

input

Z water surface elevation above datumr fluid densitytxb; tyb bed shear stress components in the x and y

directionstxw; tyw surface wind shear stress components in

the x and y directions

S.M. Kashefipour et al. / Water Research 36 (2002) 1854–1868 1855

Transport in Estuaries and Rivers), and a 2-D model,

namely DIVAST (Depth Integrated Velocities AndSolute Transport). The FASTER model was developedto simulate flow and solute transport in rivers and

narrow estuaries where a predominant flow directionexists. The DIVAST model was developed to simulateflow and solute transport in estuarine and coastalwaters, where a near horizontal flow field may be

assumed.The model FASTER can be used to simulate the

hydrodynamic, solute and sediment transport processes

in well-mixed rivers and narrow estuaries. The hydro-dynamic module of the model is based on the solution ofthe St Venant equations of mass and momentum

conservation. These equations are solved through animplicit finite difference scheme, with a varying grid sizeset-up over a space-staggered grid. For the water qualitymodule a finite volume scheme has been applied to solve

the advective-diffusion equation (ADE) [2] for predict-ing the concentration of solutes and/or suspendedsediments.

The governing hydrodynamic equations for the 1-Dmodel are given as [3]

qQqx

þ TqZqt

¼ qL; ð1Þ

qQqt

þ2bQqL

A�

2bQT

A

qZqt

�bQ2

A2

qAqx

¼ �gAqZqx

� gQjQjC2AR

; ð2Þ

where Q is the discharge, A the cross-section area of

flow, qL the lateral inflow or outflow (positive for inflowand negative for outflow), Z the water surface elevation,

C the de Chezy resistance coefficient and R the hydraulic

radius, b the momentum correction factor for non-uniform vertical velocity profile, and g the accelerationdue to gravity. Likewise the 1-D ADE is given as [4]

qSAqt

þqSQqx

�qqx

ADcqSqx

¼ qLSL þ SS þ Ajd; ð3Þ

where S is the cross-sectional average solute orsuspended sediment concentration, Dc the cross-sec-

tional average longitudinal dispersion coefficient, SL thesolute or sediment concentration of the lateral inflow oroutflow, SS the cross-sectional average source or sink

term, and jd the cross-sectional average decay orgrowth rate.

The model DIVAST is a depth integrated numerical

model, developed for simulating hydrodynamic, soluteand sediment transport processes in estuarine andcoastal waters. The hydrodynamic module of the modelis based on the solution of the depth integrated Navier–

Stokes equations and includes the following effects: localacceleration, advective acceleration, Earth’s rotation,pressure gradient, wind stress, bed resistance and

turbulent shear force. For the water quality andsediment transport module, the ADE is solved for arange of water quality indicators, including salinity,

total and faecal coliforms, biochemical oxygen demand,dissolved oxygen, the nitrogen and phosphorous cyclesand algal growth. The 2-D ADE is solved using the

highly accurate ULTIMATE QUICKEST scheme [5].The governing hydrodynamic equations in the x and

y directions can be written as [6]

qZqt

þqUH

qxþ

qVHqy

¼ 0; ð4Þ

Fig. 1. Fylde Coast, Ribble Estuary and its tributaries.

S.M. Kashefipour et al. / Water Research 36 (2002) 1854–18681856

qUH

qtþ b

qU2H

qxþ

qUVH

qy

� �

¼ f VH þ gHqZqx

þtxw

r�

txb

r

þ 2qqx

%eHqUqx

� �þ

qqy

%eHqUqy

þqVqx

� �� ; ð5Þ

qVHqt

þ bqUVH

qxþqV2H

qy

� �

¼ �f UH þ gHqZqy

þtywr

�tybr

þqqx

%eHqUqy

þqVqx

� �� þ 2

qqy

%eHqVqy

� �; ð6Þ

where U; V are the depth-averaged velocities in the x; ydirections, respectively; H is the total depth of water; Zthe water surface elevation above datum; f the Coriolisparameter; txw; tyw are the surface wind shear stress

components in the x; y directions, respectively; txb; tybthe bed shear stress components in the x; y directions,respectively; r is the fluid density; and %e the depth-

averaged eddy viscosity. Likewise the 2-D ADE used forwater quality modelling can be written as [7]

qfHqt

þqfUH

qxþqfVHqy

�qqx

HDxxqfqx

þHDxyqfqy

� �

�qqy

HDyxqfqx

þHDyyqfqy

� �¼ H½jS þ jd þ jK�;

ð7Þ

where f is the depth-averaged solute concentration;Dxx;Dxy;Dyy;Dyx are the depth-averaged longitudinal

dispersion and turbulent diffusion coefficients in the x; ydirections, respectively; jS is the depth-averaged sourceor sink solute input (e.g. outfall); jd the depth-averaged

solute decay or growth rate; and jK the depth-averagedtotal kinetic transformation rate for solute. Thisequation can also be used for modelling suspendedsediment transport fluxes.

3. Faecal coliform modelling

Total coliforms (TCs) have been used for many years

in evaluating bathing water quality with respect todomestic waste. However, because of the difficultiesassociated with the occurrence of non-faecal bacteria in

this test, the use of the TC test is being graduallyreplaced by FCs and faecal streptococci as the mainbacteriological indicators [8]. The FC bacteria group is

indicative of organisms from the intestinal tract ofhumans and other animals. In recent years, FCs havebeen used by several investigators to assess the quality of

bathing water and urban streams for both outfall and/ornon-outfall sources [9–11].

In modelling FCs, the decay term in Eqs. (3) and (7) isgenerally expressed as a first-order decay function and is

included in the 1- and 2-D models FASTER andDIVAST via the following equations:

jd ¼ �kS or jd ¼ �kf; ð8Þ

where k is the coliform decay rate (/day). The responsetime, namely the time required for the water body(i.e. river or estuary, etc.) to complete a fixed percentageof its recovery, is also commonly used to represent the

growth/decay of bacteria. This parameter can be easilyderived from the first-order decay function equation ofthe form f ¼ f0e

�kt; where f0 is the initial concentra-

tion at t ¼ 0: For example, T90 is the commonly usedparameter for this purpose and is defined as the timeduring which the original organism population would

reduce by 90%. It can readily be shown that T90 can becalculated from [12]

T90 ¼2:303

k: ð9Þ

Several factors, such as sunlight intensity and dura-

tion, temperature and salinity levels, and suspendedparticulate matter concentrations, may influence thepopulation of the organisms in a water body and thus

while reporting the decay rate sampling conditions areusually specified. However, in the literature there is alarge range in the variation of the values cited for thedecay rate of FC. Falconer and Chen [6] reported that

for Escherichia coli (or E. coli), the decay value wastypically in the range of 0.05–4.0/day, whereas Fujiokaet al. (see [8]) have reported FC decay rates in the range

of 37–110/day in seawater under good sunlight condi-tions. The value for the decay rate for total or faecalcoliform has also been reported in the range of 0.0–6.1/

day for different conditions of salinity and sunlight (see[8]). Sarikaya et al. [13] measured a value of 0.45/day(i.e. T90=125 h) for the dark FC decay rate in wastestabilisation ponds. They introduced a linear relation-

ship between decay rate and solar radiation. Accordingto their relationship, for an average of solar radiation of300 cal/cm2, the decay rate calculated is 3.62/day (i.e.

T90=15.3 h). Herrera and Castillo [14] recently pub-lished two measured FC decay rates of 0.97/day (i.e.T90=57 h) and 2.54/day (i.e. T90=21.8 h) in waste

stabilisation ponds for winter and summer time,respectively. The average value of T50 (i.e. 50%reduction of the initial population) for coliform bacteria

has been reported by Montgomery [15] as 22.0 h.

4. Dynamically linked 1- and 2-D model approach

The main interest of this study was to establish the

water quality of the EC designated bathing waterslocated at the mouth of the Ribble Estuary. In order to


reduce the possible inaccuracies caused by setting up theboundary conditions required by the numerical models,

the upstream boundaries were extended to the tidallimits of the rivers Ribble, Darwen, and Douglas (Fig. 1)and the downstream boundary was located around the

25m depth contour in the Irish Sea. The length of theseaward boundary was 41.2 km, with the width of theriver boundaries being generally o10m. Such a greatvariation in the modelling dimensions made it imprac-

tical to use either a 1- or a 2-D model alone. In applyingeither of these two models individually or linking themodels statically would have introduced inaccuracies in

the model prediction results and would also haverequired a considerable amount of effort in exchangingthe data. Thus, in this study the 1-D FASTER and 2-D

DIVAST models were linked dynamically to create asingle model, in which the mathematical computationsof the hydrodynamic variables and water quality

indicators were undertaken simultaneously across theentire modelling region.

In this combined model, the two sub-models were runindividually at each time step and the required boundary

values were exchanged between the 1- and 2-D modelsacross a common reach of the estuary as shown inFig. 2. For the hydrodynamic modelling, the predicted

water surface elevations obtained using the 2-D modelwere specified at the downstream boundary condition ofthe 1-D model and the predicted velocities or discharges

obtained from the 1-D model were specified at theupstream boundary condition of the 2-D model. Forwater quality modelling, the predicted FC concentra-tions obtained from the 2-D model were transferred to

the 1-D model when the flow across the 1-D modelboundary was directed towards the head of the estuary

(i.e. landwards), whereas the predicted FC concentra-tions from the 1-D model were transferred to the 2-D

model when the flow across the 2-D boundary wasdirected seawards.

5. Model set-up and field data

The model domain consisted of 850 km2 of estuarineand coastal waters (2-D model) and about 41 km of river

channels (1-D model). The 1-D model contained 1075cross-sections distributed along the 5 reaches, with thedistance between two consecutive cross-sections gener-

ally being o50 m. To ensure a reasonable resolution atthe upstream boundary of the 2-D model, the grid sizefor the 2-D model was chosen to be 66.7 m 66.7m. The

seaward boundary was located at a longitude of311904500W and between the latitudes of 5313300600Nand 5315405000N.

An extensive programme of data collection was also

undertaken by the UK Environment Agency to providedata for calibration and verification of the model-ling system. The bathymetric data used in this study

were primarily obtained using the following threemethods:

1. Sectional data collected by conventional surveying

techniques were used along the rivers, as far down-stream as the confluence between the Ribble andDouglas.

2. For the bulk of the outer estuary, the data werecollected specifically for the model developmentusing airborne Light Induced Direction and Range(LIDAR).

3. LIDAR is unsuited for areas permanently under-water and any gaps in the LIDAR data were filledusing side-scan sonar, mounted on a survey vessel.

In the deeper waters just beyond the mouth of theRibble, bathymetric data were digitised from theAdmiralty Chart. These data were combined using

linear interpolation to give bathymetric data for thewhole of the Ribble model.

Six sets of hydrodynamic and water quality data were

collected during the winter period of 1998 and thesummer period of 1999 to include a combination ofdifferent weather and tide conditions. The generalconditions for the surveys are summarised in Table 1.

During each survey, measurements were taken at alldischarge locations and river upstream boundaries (i.e.tidal limits) for two consecutive days. A large amount of

data were collected, including: water depth, currentspeed and direction, salinity levels and concentrations ofsuspended solids, faecal and total coliforms, and faecal

streptococci. In total, there were 34 input sourcesidentified that contributed to the pollution loads of the

Velocity

Water Elevation

Common AreaBetween 1-D

and 2-D Models

1-D DownstreamBoundary

2-D UpstreamBoundary

Fig. 2. Overlapping reach and boundary conditions for 1- and

2-D models.


estuary. These included: direct discharges of treatedwastewater from treatment plants, inputs from theupstream boundary of the 3 major rivers, and inputs

from several smaller rivers and combined sewer over-flows (CSOs). Details of the measurements undertakenat these sources for the six surveys are listed in Table 2.

Four calibration points were chosen along the mainchannel of Ribble, including: 11milepost, 7 milepost,3milepost and Bullnose (see Fig. 1). Measurements atthese locations were taken only for the second day of a

survey.

6. Model calibration and verification

Measured discharges at the upstream tidal limits of

the rivers Ribble, Darwen, and Douglas were used asthe upstream open boundary conditions. The waterlevels for the seaward boundary conditions were

predicted using data acquired from the ProudmanOceanographic Laboratory (POL) and using their IrishSea model.

The main parameter used for calibration of the

hydrodynamic model was the bed roughness. Bychanging the bed roughness values and comparing themodel predictions with measured data, the model was

calibrated by choosing the best fit between the predictedresults and the measured data. In the Ribble model,different roughness values were used along different

reaches of the model domain to reflect the localconditions. For the open coastal waters it was foundthat the most appropriate value for the Nikuradse

equivalent sand grain roughness was 20mm. For the1-D model, the Manning roughness coefficient was used,with the optimum values ranging from 0.021 for thelower part of the river to 0.028 for the upper part of the

river.FCs were used in this study as the main water quality

indicator. Since the fate of FCs is influenced by many

factors, calibration of the water quality model wasgenerally more difficult than that for the hydrodynamicmodel. The accuracy of the model predictions depends

upon an accurate representation of the advection anddispersion processes, as well as on the decay rate. The

selection of the decay rate is controlled by many externalconditions. Light intensity, water temperature, salinitylevels, the existence of toxic substances and settling or

re-suspension of particulate matter are all known toinfluence the decay rate of bacterial indicators [8]. In thisstudy, different decay rates were used for (i) day- and

nighttimes, (ii) dry and wet weather conditions, and (iii)coastal and river waters.

In general, good agreement was obtained between themodel predictions and the measured data for both the

hydrodynamic and water quality calibration studies.Details of the calibration for two events are given below,including: a wet (2–3 June 1999) and dry weather

simulation (9–10 June 1999), respectively.

6.1. Wet weather survey (3 June 1999)

The distribution of individual FC inputs for thesurvey period of 2–3 June 1999 is shown in Fig. 3. The

sum of the FC loads for the whole simulation time was2.11 1016 cfu. The maximum individual input load forthis event occurred at the upstream Ribble boundary,with more than 63% of the total input. The FC load at

the upstream boundary of the river Douglas generallyaccounted for more than 50% of the total load duringdry weather conditions but, in this survey, it only

represented about 2.5% of the total load. It wasinteresting to note that, for all wet weather events, theFC loads at the upstream limit of the rivers Ribble and

Darwen were significantly larger than the correspondingdry weather periods. In contrast, the proportion of theload, which entered through the upstream boundary of

the river Douglas decreased during wet weather events.During wet weather conditions, the CSO inputs alsobecame very significant. For example, during one typicalwet weather survey about 23% of the total input load

was discharged through the CSOs.Predicted and measured water levels, and current

speeds and directions at 11 milepost are shown in Fig. 4.

It can be seen from these results that the agreementbetween the predicted and measured water levels andcurrent speeds is encouraging, with average errors of

0.06 m and 0.15m/s for the water levels and currentspeeds, respectively. The measured tidal range and

Table 1

General information of surveys

Date Tidal condition Duration of measured data Weather

3 Dec 1998 Spring 3 Dec 1998, 11:00 to 3 Dec 1998, 23:00 Dry

10 Dec 1998 Neap 10 Dec 1998, 04:45 to 10 Dec 1998, 16:40 Wet

11 May 1999 Neap 11 May 1999, 08:00 to 12 May 1999, 09:00 Wet

19 May 1999 Spring 19 May 1999, 02:00 to 20 May 1999, 03:15 Dry

3 June 1999 Average 3 June 1999, 14:00 to 4 June 1999, 03:10 Wet

10 June 1999 Average 10 June 1999, 08:30 to 11 June 1999, 09:45 Dry


maximum speed at 11 milepost for this event were 5.4m

and 1.55 m/s, respectively. Fig. 5 shows a comparison ofthe predicted and measured FC concentrations at11milepost for this event. Again, good agreement was

obtained between the predicted and measured values, ascan be seen from this figure. However, due to oscilla-tions in the measured data, the average percentage error

related to the average measured FC values over thecorresponding period was calculated to be about 25.5%.The T90 values used for this case were 72 and 85 h in the2- and 1-D areas for daytime, and 106 and 142 h for

nighttime, respectively. The average T90 value for

the entire simulation period was 102.6 h. The average

decay rate value used was 0.54/day at 141C, or anequivalent of 0.71/day at 201C. Although this value wascomparable with a decay rate coefficient of 0.73/day for

nighttime (or dark) conditions, as reported by Auer andNiehaus [16], it seemed that this value was too low forthe decay of FCs. Since FCs are extremely sensitive to

sunlight [17] and high salinity, the obtained low valuefor decay rate was thought to be due to low lightintensity and low salinity levels in the estuary (withaverage values of o10.0 ppm) during wet weather

conditions.

Table 2

List of measured inputs for all events

No. Name of the input point SDa Area Survey

3 Dec

1998

10 Dec

1998

11 May

1999

19 May

1999

3 June

1999

10 June

1999

1. Southport WwTW 3717 2089 2-D + + + + + +

2. Southport (Storm) 3717 2089 2-D +

3. Manchester Square 2957 3507 2-D + +

4. Anchorsholme 3026 4224 2-D + +

5. Fairhaven Tanks 3376 2603 2-D + +

6. Chatsworth Avenue 3012 3000 2-D +

7. Preston (Clifton Marsh)

WwTW

4554 2787 1-D + + + + + +

8. Preston (Clifton Marsh) Storm 4571 2793 1-D +

9. Surface Water Culvert (CSOs) 5013 2929 1-D + + +

10. Freckleton (CSOs) 4392 2947 1-D + +

11. Penwortham (PSs) 5106 2926 1-D + + +

12. Penwortham (CSOs) 5282 2838 1-D + +

13. PRE0022 (CSOs) 5292 2889 1-D + + +

14. PRE0028 (CSOs) 5306 2820 1-D +

15. Frenchwood (CSOs) 5418 2866 1-D + + +

16. Preston Town East (CSOs) 5488 2926 1-D + + +

17. Walton-le-Dale WwTW 5460 2820 1-D + + + + + +

18. Walton-le-Dale (Storm) 5460 2820 1-D + + +

19. Longton WwTW 4550 2470 1-D + + + + + +

20. Longton (Storm) 4550 2470 1-D + +

21. Hesketh Bank WwTW 4562 2398 1-D + + + + + +

22. Hesketh Bank (Storm) 4562 2398 1-D +

23. Croston WwTW 4810 1880 1-D + + + + + +

24. Croston (Storm) 4810 1880 1-D + +

25. Lea Gate CSOs 4980 3080 1-D +

26. Liggard Brook, Main Drain

and Wrea Brook

3835 2765 2-D + + + + + +

27. Pool Stream 4231 2701 1-D + + + + + +

28. Savick Brook

and Deep Dale Brook

4780 3000 1-D + + + + + +

29. Middle Pool 4392 2947 1-D + + + + + +

30. Carr Brook 4595 2148 1-D + + + + + +

31. Yarrow and Lostock 4660 1866 1-D + + + + + +

32. Ribble Boundary 5520 2870 1-D + + + + + +

33. Darwen Boundary 5520 2800 1-D + + + + + +

34. Douglas Boundary 4760 1250 1-D + + + + + +

aGlobal easting and northing.


Faecal Coliform loads for the survey 3/06/1999

Ribble River63.28%

Darwen River4.07%

Douglas River2.42%

YLC1.02%

Southport WwTw1.05%

Preston WwTw0.03%

Walton-le-Dale2.27%

Longton+Hesketh Bank1.00%

Small Rivers2.06%

CSOs22.80%

Fig. 3. FC input loading for survey of 3 June 1999.

-4

-3

-2

-1

0

1

2

3

4

5

6

Wat

er E

leva

tion

(m)

Model

Measured

-0.5

0

0.5

1

1.5

2

2.5

Sp

eed

(m/s

)

-50

0

50

100

150

200

250

300

350

400

22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52

Time (hours)

Dir

ectio

n (d

eg)

(a)

(b)

(c)

Fig. 4. Comparison between predicted and measured parameters at 11milepost for survey on 3 June 1999 of (a) water elevation, (b)

current speed, and (c) current direction.


6.2. Dry weather survey (10 June 1999)

The distribution of FC loads for the 10 June 1999survey is shown in Fig. 6. The total sum of the FC load

for the whole simulation time was 8.8 1014 cfu, whichis only 4.2% of the corresponding value for the wetweather event on 3 June 1999. The main input for thisdry weather event was the Douglas boundary input,

constituting about 70% of the total input load. Incontrast with the wet weather event, the FC loadsentering the estuary from the Ribble and Darwen

boundaries were very low and only constituted about10% of the total inputs.

Predicted water surface elevations, and current speeds

and directions at 7 milepost were compared with thecorresponding measured values for this survey, with thecorresponding results illustrated in Fig. 7. The predicted

water elevations show good correlation with themeasured values (see Fig. 7a), with the average errorbetween the predicted and measured values beingtypically 0.15m. Fig. 7b also shows reasonable agree-

ment between the current speed predictions and thecorresponding measured values, with the averagedifference between the model predictions and measured

values being less than 0.15m/s, which was considered tobe within an acceptable range for such a complexestuary. The measured water elevation and velocity

ranges at 7milepost were 4.7m and 1.7m/s, respectively.Good agreement was also obtained between thepredicted and measured current directions, as canbe seen from Fig. 7c, with the average error being

about 8.21.

The predicted FC concentrations at 7milepost for thisevent were also compared with the measured values, seeFig. 8. With the exception of some scattered measuredvalues, the predicted results show relatively good

agreement with the measured data. The average T90

Faecal Coliforms at 11milepostWet Weather, Tide: 3/06/1999

0

25000

50000

75000

100000

125000

150000

175000

200000

225000

20 24 28 32 36 40 44 48 52

Time (hours)

Con

cent

ratio

n (c

fu/1

00m

l)

Model

Measured

Fig. 5. Comparison between predicted and measured FC concentrations at 11milepost for survey on 3 June 1999.

Faecal coliform input loads for the survey 10/06/1999

Preston WwTw0.51%

YLC4.27%

Douglas69.78%

Darwen2.32%

Ribble7.83%

Small Rivers10.46%

Longton + Hesketh Bank WwTw1.15%

Walton-le-Dale WwTw3.25%

Southport WwTw0.43%

Fig. 6. FC input load for survey of 10 June 1999.


value used for the 10 June 1999 event, including

simulations for day and night conditions, was 72 h.The corresponding decay rate was 0.77/day at 141C oran equivalent of 1.0/day at 201C.

The average error in the water elevation and velocitypredictions for all of the six events considered at thecalibration sites was 0.18 m and 0.14m/s, respectively.The average difference between the predicted and

measured FC concentrations for all of the events atthe calibration sites was o40.0%. The average T90 valueobtained was 87 h for wet weather events, whereas this

value was about 66 h for dry weather events. Theaverage decay rates (at 201C) for day- and nighttimeconditions for the 1- and 2-D models were found to vary

from a maximum value of 2.32/day (i.e. T90=23.8 h) fordry weather events, to a minimum value of 0.71/day (i.e.T90=77.8 h) for wet weather events. This range in thedecay rates was comparable to values reported in the

literature for FC bacteria (see [8]).

7. Model applications

Since the construction or improvement of WwTWrequires significant investment, it is necessary to be able

to predict the impact of any proposed changes indomestic discharge effluents on the coastal receivingwaters. This can aid environmental managers in makingplanning and operational decisions. In this research

study, two series of model applications were undertaken:(i) baseline runs and (ii) scenario runs. The baselinesimulations were used to replicate the existing water

quality conditions in the Ribble Estuary and along theFylde Coast. Numerical model predictions were thenobtained from the scenario simulations and compared

with the corresponding baseline simulations to assess theimpact of the previous improvements of the wastewatertreatment systems and/or the impact of future improve-ments of the treatment systems on the water quality

characteristics of the local receiving waters.

- 1

-0 .5

0

0.5

1

1.5

2

2.5

3

Sp

eed

(m

/s)

- 3

- 2

- 1

0

1

2

3

4

5

6

Wat

er E

leva

tion

(m

) M o d e l

M e a s u r e d

-50

0

5 0

100

150

200

250

300

350

400

20 25 30 35 40 45

Time (hours)

Dir

ecti

on (

deg

)

(a)

(b)

(c)

Fig. 7. Comparison between predicted and measured parameters at 7milepost for survey on 10 June 1999 of (a) water surface

elevation, (b) current speed, and (c) current direction.


For the baseline simulations, a total of twelve runs

were carried out for dry and wet weather conditions, andfor spring and neap tides and different wind conditions.The hydrodynamic data and FC inputs for an event on

19 May 1999 were chosen to represent the dry weatherbaseline simulations. The hydrodynamic and input datafrom the wet period of an event on 11 May 1999 werereplaced with a part of the dry weather baseline

simulations to represent the wet weather baseline runs.The wind speed used was 10m/s, which corresponded tothe maximum value measured during the six surveys

undertaken between December 1998 and July 1999. Thetwo dominant wind directions along the coastal region,i.e. 2251 and 3151 clockwise from north, were used to

represent the southerly and northerly wind directions forall of the simulations.

A summary of the scenario simulations is illustrated in

Table 3. The main objectives of these scenario simula-tions were to assess the impact of the current new capitalworks on the bathing water quality in the RibbleEstuary and along the Fylde Coast and to quantify

any environmental improvements from these worksimprovements and also to predict the impact ofnew proposals being considered for improving the

wastewater treatment system. The impacts of the CSOinputs on the receiving water quality were alsosimulated.

The predicted FC concentrations at the four sitesalong the Ribble Estuary and Fylde Coast, including11milepost, St Anne’s Pier, St Anne’s North andSouthport (see Fig. 1), were used to compare the

results for the baseline and scenario simulations. Four

typical velocity and FC concentration distributions

along the Ribble Estuary and Fylde Coast are shownin Fig. 9.

The general results from these simulations can be

summarised as follows:

* The predicted FC levels were higher for neap tides in

the main channel than for spring tides, both for dryand wet weather conditions.

* During a spring tide, the effluent plume from theestuary spread faster and extended further than for a

neap tide. This was thought to be due to highervelocities and lower water elevations in the channelfor a spring tide.

* The worst conditions at the compliance points,including St Anne’s Pier and Southport, during dryweather conditions occurred for a spring tide,

whereas for wet weather conditions, the neap tideresults showed an increase in the FC concentrationsat these locations.

* For dry weather conditions, the faecal loads origi-nated mainly from the river Douglas, with up to 80%of the load coming from this source, whereas for wetweather conditions, the main load came from the

river Ribble, with more than 55% of the load comingfrom this source.

* During dry weather conditions, the predicted

FC concentrations at the three compliance points(i.e. St Anne’s Pier, St Anne’s North and Southport)were all significantly below the mandatory standard

required to comply with the EC Bathing WaterDirective.

Faecal Coliforms at 7milepostDry Weather, Tide: 10/06/1999

0

5000

10000

15000

20000

25000

30000

20 25 30 35 40 45

Time (hours)

Con

cent

rati

on (c

fu/1

00m

l)

Model

Measured

Fig. 8. Comparison between predicted and measured FCs at 7 milepost for survey on 10 June 1999.


* The plume shape and movements along the Ribbleestuary and Fylde coast were noticeably affected by

the wind magnitudes and directions.* The CSO inputs discharging to the river Ribble

alone were able to reproduce the maximum FC

concentrations at St Anne’s Pier and Southport, of910 and 1010 cfu/100ml for a northerly windand 1110 and 52 cfu/100ml for a southerly wind,

respectively.* Both storm water holding tanks and UV treatment

were predicted to have a significant impact onreducing the FC concentrations along the designated

bathing waters.

8. Conclusions

Details are given of a comprehensive water quality

modelling study of an estuarine and coastal system,namely, the Fylde Coast and Ribble Estuary system.The main objectives of this study were to predict the

impact of the extensive existing FC inputs and to predictany improvements on the water quality in these areas

due to future work on the wastewater treatment systems.A numerical model was developed, which dynamically

linked a 1-D river model and a 2-D coastal model, topredict water surface elevations, velocities and FCconcentration distributions along the Fylde Coast, the

Ribble Estuary and various rivers simultaneously. Themain conclusions drawn from this study can besummarised as follows:

* The dynamically linked model approach was accu-rate in predicting the hydro-environmental condi-

tions within a complex estuarine and coastal system.The integrated model was highly robust and inidealised tests was proved to be highly accuratenumerically.

* The value of decay rate for FC was influenced bymany factors and the use of a variable decay rate wasfound to be important for predicting the FC

concentrations accurately. It was confirmed that thedecay rate needed to be more rapid for day- ratherthan nighttime, and for dry weather rather than wet

weather. The maximum and minimum FC decayrates used were 2.32/day and 0.71/day at 201C,

Table 3

Summary of scenario simulations

Run Ref. Wind Scenario Inputs

DW1 Northerly Dry weather to reflect the 1997 situation

i.e. no UV disinfection system at Preston

and Southport, primary treatment only

at Hesketh Bank WwTWs

As per dry weather baseline simulation s

except at Southport, Preston and Hesketh

Bank WwTWs

DW2 Southerly

DW3 Northerly Dry weather to predict 2002 situation when

UV is commissioned at the Wigan WwTW

As per dry weather baseline simulations

except quality of the river Douglas

DW4 Southerly

WW3 Northerly Wet weather to predict the 2002 situation

when storm tanks and UV are

commissioned at Wigan WwTW

As per wet weather baseline simulations

except flow and quality of the river

Douglas

WW4 Southerly

WW5 Northerly Wet weather to predict the 2001 situation

when storm tanks at Southport WwTW

reduce storm spills

As per wet weather baseline simulations

except flow and quality at Southport

WwTw

WW6 Northerly Scenario to predict the impact of CSOs

discharging to the river Ribble

As per wet weather baseline flows for all

rivers and WwTws but zero quality, with

new 1 yr spill rates for CSOs and average

CSO quality

WW7 Southerly

WW8 Northerly Scenario to predict the impact of the

selected CSOs discharging to the river Ribble

As for WW6 and WW7, but only using

spills from Freckleton (E&W), Lea Gate

and Surface Water Culverts

WW9 Southerly


obtained for dry and wet weather conditions,respectively.

* The plume shape and movement were found to beaffected significantly by the wind magnitude and

direction.

* During dry weather conditions, the predicted FCconcentrations at the three compliance points wereall below the mandatory standards required forcompliance with the EC Bathing Water Directive.

During dry weather, the faecal inputs impacting on

Fig. 9. (a) Predicted velocity and FC concentration distributions for mid-ebb neap tide with southerly wind. (b) Predicted velocity and

FC concentration distributions for mid-ebb spring tide with southerly wind. (c) Predicted velocity and FC concentration distributions

for mid-flood neap tide with southerly wind. (d) Predicted velocity and FC concentration distributions for mid-flood spring tide with

southerly wind.


the compliance points originated mainly from theriver Douglas, which provided more than 80% of thetotal load, whereas in wet weather conditions, themain input came from the river Ribble, with a

loading of more than 55% of the total. This indicated

that both the CSO inputs and the diffuse sources werethe main reasons for non-compliance.

* During dry weather, the spring tide played animportant role in facilitating high bacterial counts

at the three compliance points, whereas for wet

Fig. 9. (Continued).


weather conditions it was found that the neap tideproduced the higher counts.

Acknowledgements

This research study was funded by North West WaterLtd. (NWW), with the assistance of the EnvironmentalAgency, North-West Region. In particular, we would

like to thank the following staff for their support andencouragement throughout the project: Julie Wakehamof North West Water, Andrew Wither, Michael Weston,

Andrew Hartland, and Hanner Green of the Environ-ment Agency.

References

[1] Council of the European Communities. Council directive

of 8th December 1975 concerning the quality of bathing

water (76/160/EEC). Off J Euro Commun 1976;L31:1–7.

[2] Kashefipour SM, Falconer RA. Numerical modelling of

suspended sediment fluxes in open channel flows. Proceed-

ings of the XXVIII IAHR Congress, 1999, Graz, Austria.

[3] Cunge JA, Holly Jr FM, Verwey A. Practical aspects of

computational river hydraulics. London: Pitman Publish-

ing Limited, 1980. 420pp.

[4] Lee HY, Hsieh HM, Yang JC, Yang CT. Quasi-two-

dimensional simulation of scour and deposition in alluvial

channels. J Hydraul Eng ASCE 1997;123(7):600–9.

[5] Lin B, Falconer RA. Tidal flow and transport modelling

using the ULTIMATE QUICKEST scheme. J Hydraul

Eng ASCE 1997;123(4):303–14.

[6] Falconer RA, Chen Y. Modelling sediment transport and

water quality processes on tidal floodplains. In: Anderson

MG, Walling DE, Bates PD, editors. Floodplain processes.

Chichester: Wiley, 1996. p. 361–98 [Chapter 11].

[7] Falconer RA. Review of modelling flow, pollutant trans-

port processes in hydraulic basins. Proceedings of the First

International Conference on Water Pollution: Modelling,

Measuring and Prediction. Southampton, UK: Computa-

tional Mechanics Publications, 1991. p. 3–23.

[8] Thomann RV, Muller JA. Principles of surface water

quality modelling control. New York: Harper Collins,

1987. 644pp.

[9] Wyer MD, Oneill G, Kay D, Crowther J, Jackson G,

Fewtrell L. Non-outfall sources of faecal indicator

organisms affecting the compliance of coastal waters

with directive 76/160/EEC. Water Sci Technol 1997;35:

151–6.

[10] Thackston EL. CSO control project modifications based

on water quality studies. J Environ Eng ASCE

1999;125(10):979–87.

[11] Young KD, Thackston EL. Housing density and bacterial

loading in urban streams. J Environ Eng ASCE

1999;125(12):1177–80.

[12] Chapra SC. Surface water-quality modelling. New York,

USA: McGraw-Hill, 1997. 844pp.

[13] Sarikaya HZ, Saatci AM, Abdulfattah AF. Effect of pond

depth on bacterial die-off. J Environ Eng ASCE

1987;113(6):1350–62.

[14] Herrera L, Castillo G. Dynamics of a waste stabiliza-

tion pond in chile. Water Sci Technol 2000;42(10–11):

83–90.

[15] Montgomery JM. Water treatment principles and design.

New York, USA: Wiley, 1985. 696pp.

[16] Auer MT, Niehaus SL. Modelling faecal coliform bacteria-

I. Field and laboratory determination of loss kinetics.

Water Res IAWQ 1993;27(4):693–701.

[17] Burkhardt III W, Calci KR, Watkins WD, Rippey SR,

Chirtel SJ. In activation of indicator microorganisms in

estuarine waters. Water Res 2000;34(8):2207–14.


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