ch5 further issues on flow in rivers

Chapter 5

Further issues on flows in rivers

ABSTRACT

This chapter considers some further practical issues in river engineering and suggestshow the CES-AES software might be used to investigate them. The issues consideredrelate to river ecology, sediment transport, geomorphology, trash screens and block-ages to culverts. A brief overview is given of 1-D, 2-D and 3-D approaches tomodellingriver flows, and sets the CES-AES software in this context. The chapter concludes withsome high level flow charts that indicate how the software might be adapted for otherspecic research purposes.

5.1 ECOLOGICAL ISSUES

Channel design should be considered in terms of the wider system and its function-ing, balancing the ecological biodiversity needs with those of flood risk management.The relationship between channel properties, water levels and habitat provides anopportunity to maximise the biodiversity value of channels (Buisson et al., 2008).Essential to this is the need to incorporate habitats for plants, invertebrates, amphib-ians, fish, birds, mammals and reptiles where possible, whilst still enabling floodflows to be conveyed with the appropriate standard of protection. For example,aquatic plants in channels provide important environmental benets (OHare et al.,2007), since they increase water depth during summer months, providing wetted habi-tat for invertebrate and fish species (Hearne et al., 1994). They also boost habitatcomplexity by providing shelter and varied flow conditions which support large num-bers of invertebrates and juvenile salmonid fish (Armitage & Cannan, 2000; Wrightet al., 2002). The implications are that all channel design and maintenance should becarried out with sensitivity to wildlife. Thus channel management requires a multi-disciplinary approach, with inputs from a range of experts such as ecologists, waterquality specialists, environmentalists and uvial engineers as well as operating author-ities (e.g., in the UK Local Authorities, Government Agencies, Internal DrainageBoards).

256 Practical Channel Hydraulics: Roughness, Conveyance and Afflux

A useful overview of existing legislation, policy and guidance supporting this fordrainage channels is provided in Buisson et al. (2008). Perhaps most important in thecontext of European and UK flood risk management are:

Water Framework Directives (2000/60/EEC) The primary environmentalobjectives are to achieve good ecological and good chemical status for surfacewater bodies in general or good ecological potential for the specic case of heavilymodied and articial water bodies. It also seeks that in general no deteriora-tion in status of the water body which will require management of the quality,quantity and structure of aquatic environments.

Habitats and Species Directive (92/43/EEC) This aims to promote the mainte-nance of biodiversity by requiring Member States to take measures to maintain orrestore natural habitats and wild species at a favourable conservation status, intro-ducing robust protection for those habitats and species of European importance.These requirements are now transposed to national laws in the UK, for example,Conservation Regulations 1994, Conservation Regulations for Northern Ireland1995.

The UK Biodiversity Action Plans (1994) These are the Governments responseto becoming a signatory of the Convention on Biological Diversity. The plan com-bines new and existing conservation initiatives with an emphasis on a partnershipapproach. It contains 59 objectives for conserving and enhancing species andhabitats as well as promoting public awareness and contributing to internationalconservation efforts. 391 Species Action Plans and 45 Habitat Action Plans havebeen published for the UKs most threatened species and habitats, and around 150Local BAPs have been published (See: http://www.ukbap.org.uk).

Making Space forWater (MSfW) Initiatives such as the UKGovernmentsMSfWadvocate flood risk management solutions that satisfy a wider range of objectivessuch as hydro-morphological, ecological and even social needs.

Outcome Measures The UK Government has established a framework of floodriskmanagement outcomemeasures to allocate resources and to guide the activitiesof flood operating authorities so they reflect MSfW and Government policy ingeneral. These include specific outcomemeasures for nationally important wildlifesites (Sites of Special Scientific Interest SSSIs) and for UKBiodiversity Action Planhabitats.

Others include Environmental Impact Assessment; Salmon and Freshwaters Act1975; Birds Directive 1979; Wildlife and Countryside Act 1981; Habitats Regulation1994; Land Drainage Act 1994; Natural Environment and Rural Communities Act2006; UK Priority Species and Habitats 2007.

Software tools such as the CES, the Physical Habitat Simulator (PHABSIM) andRiver Habitat Simulator (RHABSIM) provide support through enabling exploration ofwhat-if scenarios. Information on local velocities, flow depths and sediment concen-trations (Section 5.2) are essential inputs for identifying the likely habitat regime, andfor re-engineering channels to support a particular species. Typical measures include(Fisher, 2001):

creating pool and riffle sequences to alter velocities; planting different vegetation morphotypes to create different flow regimes;

Table 5.1 Example habitat information for various UK channel species (adapted from Natural England) (See colour plate section).


altering timing and nature of vegetation cutting to best suit species; cutting bankside vegetation to enhance exposure to natural light; channel deepening (dredging); creation of wet and dry berms; reducing soluble, insoluble and floating pollution; and introducing weirs to reduce the flow, including the use of fish (Defra/EA, 2003c)

passes to ensure upstream movement of fish.

The CES enables users to describe the local vegetation and roughness coverand to resolve the local depths and velocities whereas more traditional approaches(e.g., PHABSIM) are based on section-averaged calculations of flow and velocity(e.g., Chezy, Manning). This more localised information can be used with habitatinformation, for example:

response curves velocity/depth versus species preferences; and LIFE scores (see Extence & Balbi; 1999) methods for linking benthic macro-

invertebrate data to prevailing flow regimes (example given in Section 4.6.2)

in support of channel design.Table 5.1 provides some examples fromNatural England of species velocity, depth

and substrate preferences. This may be used together with CES predictions to identifyor promote particular habitat regimes.

5.2 SEDIMENT AND GEOMORPHOLOGICAL ISSUES

Despite being based on a rigid-bed assumption, the conveyance methodology withinCES may be used to deal with some loose-boundary topics such as sediment transportand geomorphology. This arises primarily from its capability in being able to estimatethe lateral distribution of boundary shear stress around the wetted perimeter of a pris-matic channel of any shape, as well as the distribution of depth-averaged velocity.Since b is one of the key parameters that govern sediment behaviour, this is a par-ticularly useful extension of the CES software. Furthermore, many natural channelsare characterised by a mobile bed, with bed features such as ripples, dunes, plane bedand antidunes, resulting from sediment entrainment and deposition. Sediment issuesalso feature in many drainage channels and watercourses. The links between b (orfriction factor, f ), velocity and bed forms that develop after initiation of motion froma flat bed condition in an alluvial channel are shown schematically in Figure 5.1. Thisis taken from Raudkivi (1998), and described in an introduction to alluvial resistanceby Knight (2006c) and Morvan et al. (2008).

Figure 5.2, taken from Shen (1971), shows the impact that such bedforms can haveon the stage-discharge relationship, using data from the Padma River in Bangladesh.The lateral distribution of boundary shear stress (gHSv, versus y, where Sv is the valleyslope) is shown in the inset diagram at low and high stages. At a relatively low stage, thebedforms vary across the channel beginning with ripples near the left hand bank, thendunes, to transitional plane bed and back to dunes again near the right hand bank,in keeping with the bed shear/local velocity and bed forms indicated in Figure 5.1.

Further issues on flows in rivers 259

Figure 5.1 Variation of bed shear stress and friction factor with velocity and bedforms for a sand bedriver (after Raudkivi, 1998).

Since the local bed shear stress is mainly governed by the local depth of flow, H, thedistribution is naturally related somewhat to the geometry of the cross section. At highstage, the bedforms change, in this example by generally a single category, and becomedunes, plane bed, antidunes and plane bed again. The effect of this on resistance,and hence the H v Q relationship, is profound. Since dunes have a high resistance,and plane bed (i.e., transitional flow at Froude Numbers approaching unity) have arelatively low resistance, as shown in Figure 5.1, the correspondingH vQ relationshipis seen to flatten out at high flows, leading to a more or less constant stage of 27 feet.This is much lower than might have been anticipated had only the low to medium flowdata, up to say 75,000 cfs (cubic feet per second) [i.e., 2,124 m3s1], been used in aregression equation and then subsequently used to extrapolate the H v Q relationshipup to flows of say 300,000 cfs [i.e., 8,495 m3s1]. That estimate would then havebeen higher than that which actually occurred, arising from the significant proportionof the wetted perimeter experiencing plane bed (i.e., low resistance) conditions at highstages. This illustrates the care that needs to be taken when estimating resistance forsand bed rivers and the difficulties in extrapolatingH vQ relationships without takinginto account all the relevant physical factors.

There is a large amount of literature on the subject of sediment mechanics,including the flow conditions associated with initiation of motion (e.g., Shields, 1936;Liu, 1957), equilibrium channel conditions (e.g., Ackers, 1992a; Bettess & White,1987; Blench, 1969; Cao, 1995; Cao & Knight, 1996; Chang, 1988, White et al.,1982), sediment transport rates and concentrations (e.g., Du Boys, 1879; Einstein,1942; Bagnold, 1966; Engelund & Hansen, 1966; White, 1972; Ackers & White1973; Chang, 1988; Ackers, 1990), bedforms and resistance (e.g., van Rijn, 1982 &


Figure 5.2 Stage-discharge relationship and bedforms in the Padma River, Bangladesh, at low and highdischarges (after Shen, 1971).

1984; White et al., 1980) and general behaviour as given in various textbooks (e.g.,Chang, 1988; Garde & Ranga-Raju, 1977; Raudkivi, 1998; Shen, 1971; Simons &Senturk, 1992; Thorne et al., 1997; Yalin & da Silva, 2001; Yang, 1987).

With regard to sediment transport rates, most methods typically rely on a resolu-tion of the boundary flow conditions, using bed shear stress, shear velocities, velocityand velocity gradients adjacent to the channel bed as well as the sediment propertiessuch as sediment size, density and fall velocity. This section is now aimed at demon-strating the use of the CES in estimating sediment transport rates, using one of themore recent sediment transport approaches by Ackers & White (1973) and Ackers(1990 & 1993c).


In 1973, Ackers andWhite first proposed their total material load formulae, whichare based on physical considerations and dimensionless analysis. It assumes that forsediment moving as a bed load, transport correlates with grain shear stress, and thegrain shear stress becomes less signicant while total shear stresses become more sig-nicant for finer sediments. Three dimensionless quantities, the sediment transportparameter Ggr, the particle mobility number Fgr and the particle size number Dgr, aredefined as

Ggr = qshqd50(uU

)nac = Cac(FgrAac

1)mac

(5.1)

Fgr = unac

gd50(/s 1)

(U

32 log10(10h/d50)

)(1nac)(5.2)

Dgr = d50(g(/s 1)

v2

)1/3(5.3)

where qs (m3s1m1) is the volume of sediment transported per second per unit chan-nel width and s is the sediment density. The coefcients in these formulae, which varywith Dgr, were originally based on over 1000 experiments. More recently, these havebeen updated to include additional data (Ackers, 1990& 1993), and these coefcientsare shown in Table 5.2 according to the value of Dgr. A review of the performanceof several sediment transport formulae, including that of Ackers & White, is given inChang (1988).

It should be noted that nearly all sediment transport equations have been derivedfor inbank flow conditions, typically in a single channel. Their application to overbankflows therefore requires special treatment, as some studies suggest (e.g., Atabay, 2001;Atabay et al., 2004; Atabay et al., 2005; Ayyoubzadeh, 1997; Brown, 1997; Knightet al., 1999; Knight & Brown, 2001; Ikeda &McEwan, 2009; Tang& Knight, 2001).An example is now given to illustrate how the CES can be of use in sediment transportinvestigations, for both inbank and overbank flows.

The CES shear velocity and unit flow rate outputs may be used to solve theseformulae at each point in the cross-section, to evaluate the lateral distribution of qs.Then by lateral integration of qs, the total sediment transport rate Qs (m3s1) may beobtained, where

Qs =B

0

qsdy (5.4)

Table 5.2 Coefficients to be used in the Ackers & White sediment transportequations, (5.1)(5.3).

Coefficient Dgr > 60 1 < Dgr < 60

nac 0 1 0.56 logDgrmac 1.78 1.67 + 6.83/DgrAac 0.17 0.14 + 0.23/

Dgr

Cac 0.025 log10 Cac = 2.79 log10 Dgr 0.98(log10 Dgr)23.46


It is thus possible to determine the unit sediment charge xs (ppm) i.e. the unit massflux of sediment per unit mass of flow per unit time from

xs = qsq(

s

)106 (ppm) (5.5)

and the sediment charge Xs (ppm) i.e. the total mass flux of sediment per mass flux offluid per unit time from

Xs = QsQ(

s

)106 (ppm) (5.6)

Sediment transport is highly non-linear and using an average main channel veloc-ity such as that derived from Divided Channel Methods (DCM) or Ackers coherence(COHM) approach (Section 2.5), rather than the depth-averaged velocity distribu-tion, may result in substantially different sediment transport rates. In particular,the average main channel velocity does not capture the reduced sediment transportrates at the channel banks, tending to over-predict the average velocity and hence theoverall sediment transport. The CES methodology improves the local shear velocityapproximation from the more general formula (Eq. (2.31) in Section 2.2.3),

U =gRSf (5.7)

as it is based on the locally resolved boundary shear stress, where U = o/.A hypothetical case is now considered to demonstrate the use of the CES output.

This is based on the FCF Phase A Series 2 channel, assuming a sediment d50 of 0.8 mm,a sediment density of 2650 kgm3 and a constant flow depth of 0.198 m. Figure 5.3shows the distribution of Xs with Q for the main channel only, as well as the dis-tribution of Xs assuming no main channel oodplain interaction. This illustrates theimportance of capturing the retarding effect of the slower moving oodplain flow onthe faster moving main channel flow. The xs andXs predictions show similar distribu-tions to those produced by the SKMmodel of Abril (1997) and Knight & Abril (1996)and the Xs distribution shows a similar tend to that of Ayyoubzadeh (1995), wherethe COHM method of Ackers was used to account for the oodplain main channelinteractions.

As demonstrated, although the CES methodology was primarily intended for pre-dicting the overall flow rate, outputs such as the local boundary shear stress and shearvelocities mean it is particularly useful for estimating sediment transport rates for bothinbank and overbank flows (Abril&Knight, 2002&2004; Atabay et al., 2004; Brown1997; Knight & Brown, 2001; Ikeda and McEwan, 2009). Further development andapplications of the CES may include: predicting incipient motion based on the criticalshear stress or Shields (1936) function; establishing the transverse sediment gradi-ent for channels with varying sediment sizes; incorporating the bed form roughnessas a function of the critical velocity or boundary shear stress; erosion and automaticupdating of cross-section geometry based on boundary shear stress distribution. Someof these advances could be incorporated in the current calculation set-up, for example,as a post-processing routine or as a core engine enhancement, e.g., an update to thef value provided by Colebrook-White.


0

200

400

600

800

1000

1200

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Sedi

men

t ch

arge

Xs

(ppm

)

No main channel floodplain interactions

Main channel transport taking account of interaction

Transport expressed in terms of total discharge

Abril (1995) 2D main channel

Abril (1995) 2D total

Figure 5.3 The predicted sediment charge Xs for the FCF Phase A Series 2 channel with a d50 of0.8 mm (after Mc Gahey, 2006).

Some work has already been attempted on determining channel shape and bankerosion for inbank flows, as illustrated by Cao & Knight (1998) and Yu & Knight(1998). Geomorphological issues related to overbank flows are dealt with more fullyin Ikeda and McEwan (2009), as are sediment processes, computer simulations anddesign considerations. Work in the FCF on the behaviour of graded sediments in mean-dering channels is described by Wormleaton et al. (2004 & 2005). For informationon scour at bridges, see Melville & Coleman (2000).

5.3 TRASH SCREEN AND BLOCKAGE ISSUES

A bridge or culvert structure obstructs flow in the channel, causing an increase inupstreamwater levels relative to the levels that would be expected for the same channelin the absence of the structure. This afflux can be analyzed using CES-AES and may beconsidered as part of the hydraulic performance of the structure for specified designconditions. However, additional blockage can increase the afflux, leading to higherthan expected upstream water levels. Blockages can occur in several ways, including:

Collection of oating debris at the abutments/piers or soft. This is often referredto as temporary blockage because the debris can be removed. Removal moreoften than not requires human intervention, rather than the flow clearing thedebris away;

Collection of oating debris at a trash screen placed over the inlet of a culvert; Collection of oating ice, referred to as ice jams; Accumulation of bed load at the inlet, outlet, or within a culvert barrel; Abrupt blockage by large objects such as shopping trolleys.


There are many causes of blockage, but all are a combination of a source of debristhat can cause a blockage and a means of trapping that debris. Table 5.3 provides alist of the most common factors.

The quantitative assessment of blockage potential remains difcult. There is someempirical evidence to support predictive relationships for certain types of blockage.Accumulations of large, oating woody debris (drift) were analyzed by the US Geolog-ical Survey (Diehl, 1997) in a Federal Highway Administration study based on 2,577reported drift accumulations and field investigations of 144 drift accumulations inlarge, wooded catchments. The maximum width of drift accumulations for any bridgespan is about equal to the length of large logs delivered to the bridge. Figure 5.4 showsdata used to estimate a design log length that could be used in assessing blockagerisk for a given structure. This empirical data is valuable but it is also specic to theparticular environment, blockage mechanism and bridge types studied.

Table 5.3 Common factors causing blockage of bridges and culverts.

Factors contributing to debrissource potential

Factors contributing to blockagepotential

Stream slopeUrbanisationVegetationSinuosity of streamUpstream structuresChannel widthTrash screensAccess to the channelTime since last floodUpstream channel maintenance regimeUpstream land use and riparian managementHigh rainfall intensity

Width/heightOpening ratioShapeSkewLengthExistence of piers/multiple culvertsTrash screensTrash boomsAvailability of skilled operatorsAuto screen raking

Figure 5.4 Effective width of drift-blocked spans outside the Pacific Northwest (Diehl, 1997).


In the UK, a Blockage Risk Assessment method was developed for the Environ-ment Agency South West Region in 19978. This method is reviewed by Benn et al.(2004). Its main elements are:

A spreadsheet based blockage risk model requiring various basic structural,hydrological and debris type attributes to which individual probability risks canbe assigned;

A decision tree analysis which directs the user to various actions depending on theblockage probability;

Guidance on the application of hydraulic models to model blockage.

The study identied several major problems with identifying blockage risk. Oneis the lack of data and the second is that the probability distributions of the variablesdescribing the inuence of blockage are not independent, and the associated probabil-ities are subjective. This problem is not unique to blockage, and requires a consistentrisk management framework aligned with the treatment of other risk issues.

Research underway within the Flood Risk Management Research Consortium(FRMRC2, http://www.floodrisk.org.uk) is gathering further field data on blockageand seeking to develop a probabilistic method for predicting the onset and degree ofblockage potential at pinch points in the flooding system, such as bridges and culverts.

Trash screens (for example see Figure 5.5) are often placed over the entrance ofa culvert either to reduce the amount of debris entering the culvert barrel, where itcould cause blockage, or as a security screen to prevent unauthorised access. Whilea trash screen may prevent blockage within the culvert barrel, where it is difficult toclear debris, it may simply move the location of the blockage from the barrel of theculvert or inlet to the screen.

Figure 5.5 Trash screen on the River Sheaf in Sheffield. The screen can be cleared by a mechanicalgrab suspended from the gantry visible above the inlet. (Photo: JBA Consulting, MaltbyLand Surveys).


Debris can accumulate on a screen, which may be cleared manually or usinga mechanical system. Debris accumulation can also be linked to poor inlet condi-tions (e.g., badly aligned wing-walls, zones of ineffective flow, and piers or otherobstructions).

Future development of the CES-AES may include a blockage and trash screenmodule. In the meantime, a pragmatic approach to represent blockage is to adjusta culvert or bridge cross section so as to reduce the available flow area, either byincreasing channel bed levels or reducing the opening span and soffit dimensions.Estimates of head loss associated with a screen, hs, can be calculated based on thechange in velocity head associated with the reduction in flow area through the spacesbetween the bars using

hs = Ksc(U2sc U2mc

2g

)

(5.8)

where Umc is the section average velocity in the main channel, Usc is the averagevelocity through the spaces between bars, g is acceleration due to gravity and Kscis an energy loss coefcient. The average velocity through the screen can be com-puted as Usc = Q/Asc where Q is the discharge and Asc is the flow area betweenthe bars. CIRIA (1997) recommended Ks 1.5. Representation of trash screens andblockage in hydraulic models remains an area of on going research. Experimentalmeasurements and numerical modelling indicate that head loss coefcients increasewith increasing blockage and that it may be possible to identify dimensionless rela-tionships between flow variables for trash screens (Tsikata et al., 2009) which mayassist with the calibration of simplied empirical models for 1-D analysis.

5.4 WIDER MODELLING ISSUES

Having now described the CES-AES and its use, it is appropriate to set it in the contextof other 1-D, 2-D & 3-D software systems. This Section deals briefly with some ofthe wider issues, such as the different types of computational model that are available,their basis and mathematical formulation, their commonly perceived use and purpose,together with a discussion on the different data and calibration requirements for eachtype of model, especially as the dimensionality increases from 1-D to 3-D. Although anintroduction to the use ofmodels in investigating and solving practical river engineeringproblems has already been given in Sections 2.2.1 & 2.2.2, the reader should refer tospecialists textbooks formore information. Those that are particularly relevant to riverengineers are, for example, texts by Abbot & Basco (1989), Batchelor et al. (2000),Cunge et al. (1980), Garcia-Navarro & Playan (2007), Ikeda & McEwan (2009),Nezu & Nakagawa (1993), Versteeg & Malalaskera (1995) and Vreugdenhil (1989).

5.4.1 Types of model

Table 5.4 shows a sample of the codes that are used in practice, or research, for mod-elling fluid flows. The list is not meant to be comprehensive, but is simply aimed at


Table 5.4 Some 1-D, 2-D & 3-D river modelling codes.

1-D models 2-D models 3-D models

ISIS ISIS2D CFXMIKE11 MIKE21 PHOENICSHEC-RAS RMA2 FLUENTSOBEK TELEMAC2D TELEMAC3DInfoWorks RS 1D InfoWorks RS 2D DELFT FlOW3DNOAH DIVAST

SSIMLISFLOODTUFLOW

Research codes Research codes Research codes

showing the range of some standard models and the extent of progress in Compu-tational Fluid Dynamics (CFD) over the last few decades. Whereas 1-D codes werejust starting in the 1960s, today they are commonplace and 2-D & 3-D codes arenow frequently used in solving practical problems. This does not imply that 2-D &3-D codes have superseded the use of all 1-D models, as each type of model hasits own particular use, functionality and purpose. As seen later on, 1-D models areparticularly useful in dealing with many river issues since rivers themselves are, bynature, long single-thread systems that convey water in a predominately downstreamdirection. It is also shown that the results obtained from higher dimensionality 3-Dmodels are not necessarily more accurate and, furthermore, involve considerable effortand cost in obtaining them (Wright et al., 2004). The purpose here is to identifywhere the CES-AES software fits into this framework and to consider briefly theissues that arise when using software of different spatial dimensionality, i.e., 1-D,2-D or 3-D. Before discussing these issues an overview of the equations used in eachspatial dimension is given, with particular emphasis placed on 1-D unsteady flowmodels, so that a direct comparison may be made between these and the CES-AES,even though it contains some 2-D and 3-D flow characteristics and is for steadyflow only.

i One-dimensional model equationsAs noted in Section 2.2.4, the 1-D equation for flow in an open channel relates thegravitational and frictional forces with the local and convective accelerations. Ratherthan expressing them in terms of h and U as functions of x and t, as in Eqs. (2.36)(2.38), in 1-D river modelling codes it is customary to express the continuity andmomentum equations in terms of A and Q as functions of x and t. These are knownas the St Venant equations, and may be expressed in the form:

At

+ Qx

= q (5.9)

Qt

+ x

(

Q2

A

)

+ gA(

hx

+ Sf So)

= 0 (5.10)


where the symbols have their usualmeaning, andwhere q = lateral inflow/outflow perunit length and = momentum correction coefficient. Equations (5.9) & (5.10) thusconstitute two equations for two unknowns (A & Q) and may be solved numericallyto give A and Q as functions of x and t. The area, A, and discharge, Q, are thenknown at all points along the river and with time. Examples of unsteady flow wheresuch results might be useful are those involving flood simulations along rivers (floodrouting) or tidal motion in long narrow estuaries. Knowing A and Q for all x and t,allows the depth, h, or water level, , to be determined since the A v h relationshipis known at every cross-section. Likewise, the velocity, U, may be calculated at everytime step and cross section, since U = Q/A. The 1-D nature of the equations thusreflects the mainly one-dimensional flow in a river or estuary. An assessment of therelative importance of the various terms in Eq. (5.10) is given by Knight (1981), basedon measurements in the Conwy estuary.

The classication of 1-D flows based on Equations (2.36) and (2.38) in Chapter 2may now be claried and linked to the St Venant equations above. Reverting toU and h as variables, then Eq. (2.36) gives the following classication for 1-D flows:

Sf = So hx

Ug

Ux

1g

Ut

(5.11)

steady uniform flow |steady non-uniform flow |

unsteady non-uniform flow |Chapter 2 has indicated how uniform flow and non-uniform flow (also known as

gradually varied flow) occur in steady flow, and Eq. (5.11) now indicates howunsteadyflow adds one extra term into the momentum equation, the so-called local accelera-tion, (1/g)(U/t). The 3rd and 4th terms on the right hand side of Eq. (5.11) areknown as the convective and local accelerations respectively. This method of classify-ing flows should be distinguished from an alternative method of classication based onEq. (2.38), which relates particularly to unsteady flows, as follows:

Q = Qn[1 1

So

hx

UgSo

Ux

1gSo

Ut

]1/2(5.12)

kinematic wave |diffusion wave |

full dynamic wave |For the purposes of comparing these two classications, based on Equations (5.11)

& (5.12), with the St Venant equations, (5.9) & (5.10), consider 1-D flow in a friction-less, horizontal prismatic rectangular channel, with no lateral inflow, so that Sf = 0,So = 0, q = 0 and A = bh, where b = constant. Then, by ignoring the convectiveacceleration term (the second term in Eq. (5.10)), elimination of the terms A/t andh/x by cross differentiation reduces the pair of equations to a single equation, givingthe water surface elevation, , as:

2

t2 gh

2

x2= 0 (5.13)


This represents a simple progressive wave, since a solution to Eq. (5.13) is of theform:

= a cos(kx ct) (5.14)

where a = amplitude of the wave, k = 2 /; = wavelength; = 2 /T; T =wave period, and = height of the water surface above a datum level (= h + z asbefore) and c = wave celerity (speed), given by (gh) in shallow water. Thus at itssimplest, unsteady 1-D flow in open channels may be conceived as the movement of awave-form along a channel. This idea also links up with the concept of flood routingin rivers, based on Eq. (5.12). However the movement of a flood wave is not simplyprogressive.

Kinematic waves may be regarded as those that translate without distortion ofshape, given by combining the first term in Eq. (5.12) with Eq. (5.9). Assuming thatA and h are related to Q by single valued functions, then

At

= AQ

Qt

= Qt

dAdQ

(5.15)

Substituting Eq. (5.15) into Eq. (5.9) gives

Qt

+ dQdA

Qx

= 0 or (5.16)

Qt

+ cQx

= 0 (5.17)

where dQ/dA is the kinematic wave speed, c, given by

c = dQdA

= 1BdQdh

(5.18)

The solution to Eq. (5.17), the kinematic wave equation, provided c is constant, is

Q = f (x ct) (5.19)

where f is any function. Eq. (5.19) is thus seen to represent any shape of wave, trav-elling in a positive x direction with a speed or celerity, c, given by Eq. (5.18), withoutchange in shape. This involves the inverse of the gradient of the stage-discharge rela-tionship, 1/(dh/dQ), and the water surface width, B. Since the CES can provide thestage-discharge relationship for rivers with any prismatic cross-sectional shape, thismaybe demonstrates another use to which the software may be put, as described laterin Section 5.4.3.

However, in reality the effect of friction tends to attenuate and delay the passage ofa flood wave, so that the simple wave form suggested by Eq. (5.19) becomes distortedas the wave translates (moves) along the river. The non-uniformity of the cross-sectionshape with stage is also particularly important in this respect. To account for this, the


first two terms in Eq. (5.12) may be combined with Eq. (5.9) to give the so-calledconvective-diffusion equation, usually presented in the standard form as

Q t

+ c Q x

= D2Q

x2(5.20)

in which c = kinematic wave speed, given again by Eq. (5.18) and D = diffusioncoefficient, given by

D = Q(2BSo)

(5.21)

It follows from Eq. (5.20) that the discharge in a channel during a flood event hasthe characteristics of a wave that now translates and attenuates. It should be notedthat in the context of river engineering, both c and D in (5.20) are functions of thedischarge Q, making the solution of Eq. (5.20) somewhat difficult. However, Cunge(1969) has shown that the Variable Parameter Muskingum-Cunge (VPMC) methodis similar to the Preissmann finite-difference scheme used in solving the St Venantequations (Preissmann, 1961). VPMC uses 4 points in x-t, like the Preissmann boxscheme but the leading term of the numerical truncation error is tuned to match thediffusion term. This then relates Equations (5.9) & (5.10) with (5.20) and allows forkinematic wave routing methods to be used in practice for channels with bed slopesgreater than around 0.002 (See Knight, 2006c, for further details). It also gives atheoretical basis for varying the travel time constant, K, and the distance weightingparameter, , in the basic routing equations used in the original Muskingum method(Bedient & Huber, 1988; Shaw, 1994):

I O = dSdt

(5.22)

S = K[ I + (1 )O] (5.23)

where I = inflow, O = outow and S = storage. In the Constant ParameterMuskingum-Cunge method (CPMC), the travel time and distance weighting para-meters are regarded as constants, to be determined for a particular river by backanalysis of flood level data for at least one typical flood event. In the Variable Param-eter Muskingum-Cunge (VPMC) the K and values vary for each reach, stage andtime step, thus modelling the flood wave more accurately. See Tang et al. (1999a & b,2001) and Knight (2006c) for further details.

ii Two-dimensional model equationsThe shallow wave water equations are generally used in 2-D models expressed byMorvan et al. (2008) in terms of one mass conservation equation and two momentumconservation equations:

ht

+ hUdx

+ hVdy

= Ql (5.24)


hUdt

+ hU2d

x+ hUdVd

y+ ghh

x= gh(Sox Sfx) (5.25)

hVdt

+ hUdVdx

+ hV2d

y+ ghh

y= gh(Soy Sfy) (5.26)

with

Sfx =n2Ud

U2d + V2dh7/3

and Sfy =n2Vd

U2d + V2dh7/3

(5.27)

where Ud and Vd are the depth-averaged velocities in the x and y directions respec-tively. Other terms can be included to represent the effects of turbulence. If theturbulent terms are omitted they are in effect lumped together with the numericaltruncation errors and any other calibration coefcient, rather than truly reecting thephysical process. The determination of n is therefore not straightforward and thisraises a number of issues, as discussed by Cunge et al. (1980), Samuels (1985) andMorvan et al. (2008).

Equations (5.24)(5.26) thus constitute three equations for three unknowns(Ud, Vd and h) andmay be solved numerically to give these as functions of x, y and t. Inthis case, rather than use cross-section data, a mesh of {x, y} co-ordinates are requiredto represent the river and any associated oodplains. The individual velocity vectorsmay be combined to give the magnitude and direction of the depth-averaged velocityat any point with time. This is one advantage of such a model compared with the CES-AES, in which the direction of flow is assumed to be always in the streamwise direction.A 2-D model is therefore more suitable where details of flows over oodplains arerequired, or where the geometry of a river is such that the flow is complex, as in thecase of bends or braided rivers for example. However, the 2-D model, as expressedin Equations (5.24)(5.26), cannot deal with secondary flows, plan form vorticity orReynolds stresses, which the CES-AES can do in an approximate, but effective manner.

iii Three-dimensional model equationsIn 3-D models, the three time-averaged RANS equations, together with one equationfor mass conservation, are used determine all three velocity components {UVW} atevery point {xyz}, together with the depth, h, as a function of {xy} in plan form, withtime, t. In essence, 4 equations are solved to give the 4 unknowns {U, V, W & h} asfunctions of {x, y, z & t}. A 3-dimensional mesh is therefore now required to repre-sent the model river, with special treatment being taken for the free surface (initialwater level throughout the domain, as well as how it is controlled during the compu-tation). See Ikeda & McEwan (2009), Nezu and Nakagawa (1993), Rodi (1980) andVersteeg & Malalaskera (1995) for further details.

Such models are inevitably mathematically complex, and also require a large num-ber of ancillary equations to deal with the turbulent structures, in what is known as theturbulence closure problem. It is referred to as a problem because of the difcultyin describing turbulence in fluid flows, based on our incomplete knowledge of thephysical reality, as well as our inability to describe such turbulence in a mathematical


set of equations that are universally agreed. Turbulence closure relates the unknownturbulent processes to the larger scale resolved motions. For further information onturbulent energy, cascades and spectra see Tennekes & Lumley (1972) and Versteeg&Malalaskera (1995).

The turbulence closure issue has been addressed by many researchers, usually byformulating an equation (or set of equations), that purports to represent the turbulence.These additional equations may be of many types. The simplest is a zero equationmodel, based either on the eddy viscosity principle, first proposed by Boussinesq in1877, or themixing length hypothesis, first proposed by Prandtl in 1925. TheCES-AESin fact uses the same eddy viscosity approach to deal with one aspect of turbulence,as already indicated in Eq. (2.59) or Eq. (3.18). Other types of equations to dealwith turbulence closure problem are: a two-equation model, involving differentialequations that represent the relationship between energy production and dissipation;a Reynolds stress model that accounts for all three internal turbulent stresses; or, analgebraic stress model that in a somewhat simpler manner accounts for the anisotropyof the Reynolds stresses. Finally, some have even suggested that the time-averagedRANS equations may not even be the right starting place for certain types of problemand that double-averaged equations (i.e., averaging over time and space) should beused instead (Nikora et al., 2008). As an alternative to RANS based models, largeeddy simulation (LES) models or Direct Numerical Simulation (DNS) may be used toactually account for the individual turbulent fluctuations. The present position is bestsummed up by the authors of two books, written some 70 years apart, as cited inNakato & Ettema (1996, pages 457458) by one of the present authors:

Were water a perfectly non-viscous, inelastic fluid, whose particles, when inmotion, always followed sensibly parallel paths, Hydraulics would be one of themost exact of the sciences. But water satises none of these conditions, and theresult is that in the majority of cases brought before the engineer, motions andforces of such complexity are introduced as bafe all attempts at a rigorous solu-tion. This being so, the best that can be done is to discuss each phenomenon onthe assumption that the fluid in motion is perfect, and to modify the results soobtained until they fit the results of experiments, by the introduction of someempirical constant which shall involve the effect of every disregarded factor. It isworthwhile here impressing on the student of the science that, apart from theseexperimentally-deduced constants, his theoretical results are, at the best, onlyapproximations to the truth, and may, if care be not taken in their interpretation,be actually misleading. On the other hand, it may be well to answer the criticismof those who would cavil at such theoretical treatment, by pointing out that theresults so obtained provide the only rational framework on which to erect themore complete structure of hydraulics.(A.H. Gibson, 1908)

Turbulent motions contribute signicantly to the transport of momentum, heatand mass in most flows of practical interest and therefore have a determininginuence on the distribution of velocity, temperature and species concentrationover the flow field. It is the basic task of engineers working in the field of fluidmechanics to determine these distributions for a certain problem, and if the task is


to be solved by a calculation method, there is no way around making assumptionsabout the turbulent transport processes. Basically this is what turbulence mod-elling is about: because the turbulent processes cannot be calculated with an exactmethod, it must be approximated by a turbulence model which, with the aid ofempirical information, allow the turbulent transport quantities to be related tothe mean flow field.(W. Rodi, 1980)

As can be readily appreciated from these comments, as well as from the governingequations, that as one moves from 1-D to 3-D models, so does the level of complexityincrease markedly. This is especially so in the ancillary equations required to describethe turbulence in mathematical terms. Another feature of 3-D models is the largenumber of physical constants that need to be known in order to calibrate, or indeedvalidate, the model at all. This then suggests that for practical purposes, one shoulddecide on the purpose for using a model, before embarking on any modelling at all.One should then select the most appropriate model carefully, commensurate with thepurpose in mind, the models technical fitness for the task, as well as the ease withwhich it may be used.

5.4.2 Implications involved in model selection,calibration and use

It is worth stressing again that higher dimensionality of model does not necessarilylead to better accuracy in the results. In certain cases the opposite may be true. Theprinciple of Occams razor should always be applied that of starting with the simplestby assuming the least. Pluralitas non est ponenda sine necessitate; Plurality shouldnot be posited without necessity. (William Occam, 12851347). The principle givesprecedence to simplicity; of two competing theories, the simplest explanation of anentity is to be preferred. The principle may also be expressed as Entities are not tobe multiplied beyond necessity. [Encyclopaedia Britannica, 2003].

In the context of modelling flows in rivers, this suggests caution before embarkingon 3-D modelling for solving every type of river engineering problem. As has beenseen for flood routing, a 1-D model might be quite adequate for estimating traveltimes of floods in a river with fairly regular floodplain geometry. However, where ariver meanders significantly, and any overbank flow is not aligned with the flow inthe main river channel, then a 2-D depth averaged model will be more appropriateand might be sufficient for the purposes of estimating the magnitude and directionof the velocities at various points in the river valley. The trend in Europe and morerecently in the UK is a move towards coupled 1-D and 2-D models, where the 1-D model simulates flow in the main river channel and the 2-D model captures thefloodplain flow effects. Where detailed flow patterns are required, as for example inpump fore-bays at river intakes, or in heat dissipation and re-circulation modellingstudies for a power station, then a 3-D model might be the only possible and sensibleoption.

Little mention has been made so far about boundary conditions in 1-D, 2-D &3-D models. These not only differ according to the type of differential equations beingsolved, but also influence the solution methodology, the results and the data required


to run the model at all. For example, in 1-D unsteady flow models, the boundarycondition upstream is normally that of an input hydrograph {Q v t} and the boundarycondition downstream is frequently that of a fixed depth or a {h v Q} relationship.Initial conditions will also be required throughout the domain, e.g., starting valuesfor depths and discharges (or velocities) at all cross sections. In 2-D models, similarrequirements apply, but at open boundaries the velocity field must be specified as well,which may initially be unknown. The same applies in 3-Dmodels where wall functionsare used to mimic the boundary conditions at mesh points very close to the wall, andcyclic boundary conditions might be applied at open ends to improve convergence andreduce the run time. For further information on boundary conditions, see Cunge et al.(1980), Samuels (1985) and Vreugdenhil (1989).

The numerical approach used in solving any governing differential equation, pos-sibly based on finite-difference, finite-element or finite-volume methods, also needsto be appreciated and understood. The consistency, convergence, stability and accu-racy of any adopted numerical method needs to be known, and possibly also thetechniques used in investigating them. Such techniques are often based on Fourierseries in complex exponential form, but are clearly beyond the scope of this book.See Abbot & Basco (1989), Cunge et al. (1980) and Preissmann (1961) for furtherinformation.

In 1-Dmodels, it should be noted that even the governing St Venant equations maybe discretized in differentways, and that this will inevitably affect the numerical results,as shown by Whitlow and Knight (1992). In 2-D and 3-D models it is imperative tovary the mesh size and to observe at what point of resolution any two solutions agree.In 3-D models, it is not only important to vary the mesh size, but also to check onkey parameters at strategic points in the mesh, monitoring them at certain time stepsas the computation proceeds. This is because the numerical procedure may take manythousands of iterations to fully converge to a solution, even when solving for a steadyflow case. For example, the turbulent intensity or Reynolds stress at a certain pointin the mesh is influenced by values at adjacent points in the mesh. Since the solutiontechnique is iterative, the parameters at a single point (e.g., 3 velocity components,{UVW}, mean pressure, 3 turbulent intensities, 3 Reynolds stresses, etc.) may take along time to converge at that particular point, and at every other point in the mesh(often thousands). The numerical solution for the whole domain is then not completeuntil similar convergences are obtained at all points in the mesh, to a pre-determinedand specified accuracy for each particular variable or parameter.

From the preceding paragraph it is clear that the data required for different typesof model varies according to their dimensionality and complexity. A 1-D model willgenerally only require cross section data at specified intervals, chosen to reflect theterrain, as indicated by Samuels (1990), and more recently by Casellarin et al. (2009).Channel roughness (and its representation) is arguably the single most important issueto resolve prior to successfulmodelling. In 1-Dmodels, the use of channel cross sectionsmakes it relatively easy to compare results with knownmean velocities, sinceU = Q/A.In 2-D models, the data required for calibration increases significantly, since twocomponents of velocity are required at every point in the mesh, giving both magnitudeand direction locally. Furthermore, in 2-D models the water surface itself may nolonger be considered as planar, as for example occurs in bend flow or in the vicinityof bridges. In 3-D models the data requirements multiply even more significantly,


with mean and turbulent parameters required at every location in an {xyz} co-ordinatesystem.

In most practical case studies there is simply not enough hydraulic or turbulencedata at sufciently detailed spatial and temporal scales to be meaningful. This makesthe calibration stage particularly difcult, and reliance has then to be placed on thesoftware users experience. The dimensionality of the model also affects the run timeand data handling. Modern CFD packages usually come with a number of post pro-cessing packages, enabling the results to be analysed in several ways. The use ofgraphs and charts to plot secondary flow cells, strength of vorticity, shear stresseson internal elements, colours to visualize velocity fields within each cross section,etc., make it possible to undertake assessments and any comparisons quickly and ef-ciently. Comparisons may not always be between measured and numerical results,on account of the scarcity of measured data, but often between different computerruns with differing flow or geometric features. Not to be forgotten are comparisonswith any analytical results, if they are available, and with any suitable benchmarklaboratory data.

The concept of equifinality is an important one in model calibration, as notedby Beven & Freer (2001), who state that

. . . the concept of equifinality is that the uncertainty associated with the useof models in prediction might be wider, than has hitherto been considered, sincethere are several (many?) different acceptable model structures or many acceptableparameter sets scattered throughout the parameter space all ofwhich are consistentin some sense with the calibration data. The range of any predicted variables islikely to be greater than might be suggested by a linearised analysis of the areaof parameter space around the optimum. This suggests that the predictionsof all the acceptable models should be included in the assessment of predictionuncertainty, weighted by their relative likelihood or level of acceptability.

The use of models outside their calibration range also raises issues in assessinguncertainty. See Beven, (2006), Hall & Solamatine (2008), Samuels (1995) and Sharifiet al. (2009a & b) for further details.

5.4.3 The CES-AES software in context

The CES-AES software may now be reviewed in the light of the two preceding Sections.Firstly it needs to be stressed that the model is for steady flow and is more than a 1-Dmodel, since it has certain 3-D flow features embedded in it. It does therefore not fitneatly into the classification given in Table 5.4. It may be described as a type of lateraldistribution model (LDM) for both inbank and overbank flows, as it simulates thesein a unified manner by treating the flow as in Nature, as a continuum. The same threecalibration parameters f , and are used in both types of flow, thus unifying themethodology. It perhaps might be regarded then as a 1.5-D model with 3-D features.

These 3-D features are the ability to model secondary flows for inbank flow via ,lateral diffusion via a dimensionless eddy viscosity, , planform vorticity in overbankflow via, and boundary shear stresses via a local friction, f . Themodel was developedon the basis of large scale experiments carried out in the Flood Channel Facility (FCF),and validated against an extensive range of experimental data, including detailed


turbulence data on Reynolds stresses, lateral distributions of velocity and boundaryshear stress at the same flows in comparable geometries, making the determination oflocal and zonal friction factors possible, and varying oodplain roughness. The CEShas also been tested against a number of full scale rivers in the UK and overseas, forwhich there is measured data, as well as other laboratory data. See Mc Gahey et al.(2006 & 2008).

The Roughness Advisor (RA) distinguishes the CES-AES from other software,as the resistance term is known to be one of the dominant terms in the St. Venantequations, requiring special care. The addition of an Afflux Estimation System (AES)allows the user to include the second most important feature affecting water levels andcontributing to head losses, namely bridges and culverts. Togetherwith theUncertaintyEstimator (UE), it allows the CES-AES to be applied to practical river engineeringproblems with a degree of confidence not matched by comparable systems.

The AES combines many novel features by treating bridges and culverts in acomprehensive way, with multiple arches and backwater effects. One particularlydistinguishing feature is that the experimental data for bridge afflux were obtainedfrom experimental flows in compound channels, rather than basing it on flows in sim-ple rectangular channels, as previous authors have done. This is more representativeof the actual conditions occurring in practice. The data used herein for both bridgeafflux and compound channels are thoroughly documented and available via the twowebsites www.flowdata.bham.ac.uk and www.river-conveyance.net.

The examples in Chapter 2, showing how 4 or 6 panels can produce lateral distri-butions of depth-averaged velocity and boundary shear stress in trapezoidal channelsof a comparable standard as produced by any 3-D model, but with much less effort,are remarkable. The number of panels or slices in the depth-averaged model naturallyincreases when the CES is applied to flows in rivers, especially where the need is toinvestigate velocity fields due to different vegetation patterns. The ability to estimatelateral distributions of boundary shear stress are particularly useful when dealing withsediment behaviour in rivers and channels. This is perhaps one of the most signicantuses to which the CES-AES might be put in the future.

Despite being designed initially for steady flow, some elements of the CES-AESmake it applicable to unsteady flows, noticeably the use of the conveyance, K, via Eqs(2.41) and (3.48), and subsequent discharge estimation, provided the correct watersurface slope is used. The prediction of an accurate stage-discharge relationship alsofeeds into the estimation of a reliable wave speed- discharge relationship (c v Q) viaEq. (5.18), as demonstrated by Tang et al. (2001).

5.5 SOFTWARE ARCHITECTURE AND CALCULATIONENGINES

The conveyance and afflux estimation projects both included a requirement to pro-duce a software tool that implemented the new estimation method. This led to thedevelopment of the CES-AES software that is available for download at www.river-conveyance.net. This software is intended to be a relatively simple application thatprovides the user with a simple and intuitive means of using the new conveyance andafflux methods on any appropriate set of data.


Figure 5.6 Overall structure of the CES-AES software.

Figure 5.7 Roughness calculation engine.


Figure 5.8 Conveyance calculation engine.

The overall structure of the software from a user perspective is shown in Figure 5.6.The software incorporates 3 main elements, the roughness advisor, the conveyancegenerator and the backwater calculator. Afflux is calculated as part of a backwateras this provides the necessary downstream water level for the afflux method. Dataspecific to the site of interest is stored in two data files. The .RAD file contains theuser specific data relating to vegetation, substrate and irregularity for each of theroughness zones of interest. This file is saved by the user and stores the output ofthe roughness calculations carried out within the roughness advisor. The .GEN filecontains the geometrical data for the channel and any bridge and culvert structures,as well as information on which roughness zones are used for calculating the sectionconveyance. Together the .GEN and .RAD files contain all of the data relevant to aparticular site.

The raw data for the roughness advisor is provided from a number of databasesthat capture the outputs of the roughness review carried out for the original conveyanceestimation project. These databases are in a simple .CSV format and this was selectedto allow users the flexibility to edit and update the files if they have access to improvedor alternative roughness data, though for most purposes the data should be consideredas fixed.


Figure 5.9 Backwater calculation engine (case without structures).

Figures 5.7, 5.8, 5.9 and 5.10 show the flow charts that define the operation ofthe calculation engines for roughness, conveyance and backwater.

The majority of users of the CES-AES will run the software through the standarduser interface. However, it was anticipated in both the conveyance and afflux projectsthat the source code for the calculation methods would be made available for the pur-poses of further research. The software design allows for this by separating all themain control and calculation code from the user interface. This is essential, as the userinterface contains compiled proprietary code that cannot be distributed. The currentstructure of the software when compiled is shown in a simplied form in Figure 5.11.The main software procedures are all handled by the Convey.DLL module, includingfile access, data structure and manipulation, roughness calculation, backwater calcu-lation and interfacing to the conveyance and afflux calculationmodules. This structure


Figure 5.10 Afflux calculation engine.

Figure 5.11 Underlying structure of the CES-AES software.


effectively allows a programmer with access to the source code to replace the standarduser interface with a simple interface of his or her own, and run all of the underlyingcalculation code.

The Afflux calculation is accessed through an interface (AEData). This structurewas adopted as it allowed the afflux calculator to be developed independently fromthe CES software, with interaction taking place through the agreed software interface.

In practice, the overall structure shown is more complicated at the level ofthe source code itself, with the code sub-divided into classes in line with goodsoftware development procedures. The code has been written in three different lan-guages. The core conveyance calculation engine (ConveyCalcs) is currently writtenin C for maximum portability, whilst the afflux interface and engine are written inVB. All other parts of the software are written in C++. It is probable that futureupdates to the software may result in changes to this structure though these willnot remove the capability of running the calculation modules from outside the userinterface.

Customisation of the software can be carried out at three levels. Firstly, the usercan modify the default parameters that are used in the conveyance calculation. TheAdvanced Option box shown in Figure 5.12 provides a means for users to alter manyof the model parameters via the CES-AES User Interface (Table 5.5).

Figure 5.12 Advanced options for conveyance calculation method.


Table 5.5 Advanced options and default values.

Advanced options Default value Allowable range

Temperature 15C >0Number of depth intervals 25 100Minimum depth used in calculation Lowest bed elevation Any depth below maximum

entered elevationLateral main channel eddy viscosity mc 0.24 0.10.5Number of vertical segments used in 100 500

integrationRelaxation parameter 1 0.5 Relaxation 1.5Convergence tolerance 0.001 0.0011.000Maximum number of iterations 20 50Wall height multiplier 1.5 10Experimental flume Off On/off

Secondly, the user may modify the default roughness databases used by thesoftware. These changes might take the form of adjustments to the default rough-ness values, incorporation of new photographs or addition of new materials androughnesses. The file formats are fully documented and this documentation can bedownloaded from the CES-AES Website.

Thirdly, it will be possible for researchers to investigate changes to the corecalculation routines in the software through modication of the software source code.For example, the management models used to implement cutting of vegetation mightbe modied in the light of improved knowledge of the re-growth rates of various vege-tation types. The existing cutting routines are found in the CManagementModel classand the C++ code that implements this is available as ManagementModel.cpp.

At the time of writing, the mechanism by which source code for the CES and AEScalculation may be made available had yet to be finalised. However, it is expectedthat it will be made available under licence to other software houses and bona fideresearchers. Further information will be available through the CES-AES website atwww.river-conveyance.net.

5. Further issues on flows in rivers5.1 Ecological issues5.2 Sediment and geomorphological issues5.3 Trash screen and blockage issues5.4 Wider modelling issues5.4.1 Types of model5.4.2 Implications involved in model selection, calibration and use5.4.3 The CES-AES software in context

5.5 Software architecture and calculation engines

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ch5 further issues on flow in rivers

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