morphodynamic river processes and techniques for assessment of channel evolution in alpine gravel...

2007) 340355www.elsevier.com/locate/geomorph

Geomorphology 90 (

Morphodynamic river processes and techniques for assessment ofchannel evolution in Alpine gravel bed rivers

E. Formann , H.M. Habersack, St. Schober

Department of Water Atmosphere Environment, Institute of Water Management, Hydrology and Hydraulic Engineering,BOKU University of Natural Resources and Applied Life Sciences Vienna, Muthgasse 18, 1190 Vienna, Austria

Received 30 November 2005; received in revised form 22 May 2006; accepted 18 October 2006Available online 25 April 2007

Abstract

Over the past 10 years many restoration projects have been undertaken in Austria, and river engineering measures such as spurdykes and longitudinal bank protection, which imposed fixed lateral boundaries on rivers, have been removed. The EU-Life ProjectAuenverbund Obere Drau has resulted in extensive restoration on the River Drau, aimed to improve the ecological integrity ofthe river ecosystem, to arrest riverbed degradation, and to ensure flood protection. An essential part of the restoration designinvolved the consideration of self-forming river processes, which led to new demands being imposed on river management.

This paper illustrates how model complexity is adapted to the solution and evaluation of different aspects of river restorationproblems in a specific case. Point-scale monitoring data were up-scaled to the whole investigation area by means of digitalelevation models, and a scaling approach to the choice of model complexity was applied. Simple regime analysis methods and 1-Dmodels are applicable to the evaluation of long-term and reach-scale restoration aims, and to the prediction of kilometre-scaleprocesses (e.g. mean river bed aggradation or degradation, flood protection). 2-D models gave good results for the evaluation ofhydraulic changes (e.g. transverse flow velocities, shear stresses, discharges at diffluences) for different morphological units at thelocal scale (100 m10 m), and imposed an intermediate demand on calibration data and topographic survey. The study shows thatcomplex 3-D numerical models combined with high resolution digital elevation models are necessary for detailed analysis ofprocesses (1 m0.01 m), but not for the evaluation of the restoration aims on the River Drau. In conclusion, model choice(complexity) will depend on both lower limits (determined by the complexity of processes to be analysed) and upper limits (fielddata quality and process understanding for numerical models). 2007 Elsevier B.V. All rights reserved.

Keywords: River restoration morphodynamics; Assessment techniques; River widening; Monitoring; Numerical modelling

1. Introduction

From 1999 to 2003 under the European Life ProjectAuenverbund Obere Drau (Restoration of the wetlandand riparian area at the Upper Drau River), restoration

Corresponding author.E-mail address: [email protected] (E. Formann).

0169-555X/$ - see front matter 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.geomorph.2006.10.029

measures were implemented for a river length of 7.6 km.The aims of these restoration measures were to improvethe ecological functioning, to stabilise the river bed ofthe Drau through promoting ecologically-sustainabletypes of river engineering (removing protection struc-tures from the river banks in order to widen the riverbed), and to ensure flood protection (Bundeswasserbau-verwaltung, 2004).
mailto:[email protected]://dx.doi.org/10.1016/j.geomorph.2006.10.029

Table 1Simulation approaches depending on the dimensionality, requireddifferential equations and spatial resolution

Dimension Differentialequation

Resolution

DNS 3 4 1 mmLES 3 4 1 cmRANS 3 4 1 dm3D hydrostatic 3 3 1 mDepth averaged 2 3 10 mCross section averaged 1 2 100 m

DNS, Direct simulation, LES, Large eddy simulation, RANS,Reynolds averaged Navier Stokes equation (Malcherek, 2001).

341E. Formann et al. / Geomorphology 90 (2007) 340355

In this context biotic (e.g. fish, birds, amphibians,vegetation) and abiotic (river bed morphology) moni-toring programs were established to document theeffects of the restoration project. The monitoring ofabiotic processes was undertaken by the Institute ofWater Management, Hydrology and Hydraulic Engi-neering at the University of Natural Resources andApplied Life Sciences (BOKU), Vienna (Habersacket al., 2003). The objectives of this abiotic monitoringwere to document and analyse the changes in rivermorphology, substrate, water levels and flow velocitiesdue to the restoration measures. This was carried outduring the years 1999 to 2003 (1 year before and 3 yearsafter the restoration measures) in three restorationsections, which are called Spittal, Kleblach and Dellach.

However, monitoring based on field measurementsalways gives limited spatial information, depending ontime and scale. Thus, numerical models are applied,firstly to increase the spatial density, secondly to provideadditional explanatory information (e.g. Bradbrooket al., 1998) and thirdly to simulate processes generatedby different combinations of boundary conditions (Laneet al., 1999). Therefore physicalnumerical interpola-tion methods are used to upscale monitoring data to thewhole investigation area. This allows a complex analy-sis of processes (abioticbiotic) and prognosis ofmorphological developments regarding different bound-ary conditions (e.g. hydrological, hydraulic and sedi-ment parameters).

Different numerical width adjustment models exist(e.g. Molinas and Yang, 1986; Chang, 1988a,b; Van DeWiel and Darby, 2004; Rinaldi and Darby, 2005), butthere is no universal model applicable to all the circum-stances under which adjustments (e.g. bank failure,fluvial erosion, vegetation effects) and dynamic changesmay occur. Furthermore, the choice of a numericalmodel depends on the aims to be analysed andevaluated. Lane et al. (1999) suggest that model choiceshould consider dependencies amongst a) requirementsof modelling, b) scale of model application, and c)processes included in the model. They note that the scaleat which a particular field observation is made shouldmatch the scale to which model predictions relate.Table 1 (based on Malcherek, 2001) presents differentsimulation approaches depending on spatial dimensionsand resolution, and the differential equations thereforerequired.

Spatial resolutions of 100 m to 10 m require 1-D or 2-Dmodels. If 2-D models are used at spatial resolutions ofthe order of 0.01 m then the numeric simulation mayproduce invalid results (turbulence which does not existin nature). Of course, a 3-D model provides a more

reliable estimate of the hydraulics, representing theeffects of flow structure (caused by secondary circula-tion) and the three-dimensional flow field for mixingprocesses (Lane et al., 1999). But 3-D simulation mo-dels impose high demands on field data (e.g. boundaryconditions, high resolution topographic survey), in-creasing the need to understand fundamental assump-tions of the model for its correct application. If the fielddata are not of adequate quality, and/or there is no needto consider 3-D flow fields, then it is appropriate to usemodels of lower complexity and dimensionality in rivermanagement decision-making.

Furthermore, many processes (bank failure, vegeta-tion cover, and sediment transport) are currently onlydescribed by 1-D approaches. Further investigation isnecessary to implement complex phenomena such as therole of riparian vegetation (e.g. Abernethy and Ruther-furd, 1998; Simon and Collison, 2002; Van De Wiel andDarby, 2004), and bank hydrology (e.g. Rinaldi andCasagli, 1999; Rinaldi et al., 2004) in modellingsoftware. Fully-coupled interactions amongst differentprocesses are often not considered (Rinaldi and Darby,2005), and most reviews tend to reduce their approach tofocus on a single set of processes.

This study illustrates some applications of approachesto channel evolution, and shows the need for models ofreduced complexity to evaluate river restoration mea-sures. First, assessment techniques and numerical modelsare tested against the monitoring data set and are verifiedusing a river scaling concept (Habersack et al., 2000).Secondly, the achievements of the restoration aims areevaluated using calibrated models of reduced complex-ity, appropriate for the complexity of the restorationaims. The evaluation questions which have to be an-swered in this study are based on different levels ofprocess representation, and these levels defined thelower limit of model application. The main objective ofthis paper is to discuss the various levels of complexity

342 E. Formann et al. / Geomorphology 90 (2007) 340355

in monitoring and modelling required to analyse andevaluate river restoration projects.

2. The study reach

2.1. River engineering measures and changes in rivermorphology

This paper analyses one of three restored reaches ofthe River Drau, that in KleblachLind in the south ofAustria (Fig. 1). With a total length of approximately2 km, this reach was historically (before the 20thcentury) a partially-braided, aggrading channel systemwith a large sediment supply from Alpine sources(Nachtnebel et al., 1993). However, high floods at theend of the 19th century and again in the 1960s requiredsolutions for flood control and to minimise river bedaggradation. To achieve these objectives, a variety ofbank protection measures was performed, and the riverbed was channelized. This caused uniform river widthsof c. 50 m and an average water depth of c. 4.5 m at themean annual flood (300 m3 s 1). These measures,together with catchment-wide changes, caused econom-ical and ecological problems (Habersack and Nachtne-bel, 1994).

In order to improve the ecological functioning of theDrau river and to minimise river bed degradation, riverbed widening measures were implemented from 1999 to2003 (Habersack et al., 2003). This included re-opening

Fig. 1. Location map and air photograph of the study reach on

a left-bank sidearm of the river over a length of 450 m,so that the flow diverges around an island (see Fig. 1).Hence, the morphology has changed significantly, to ariver with high structural variability, with gravel bars,still water zones and islands. River widths now rangefrom 80 to 120 m, and the average water depth is c.3.5 mat the mean annual flood (c. 300 m3 s 1).

2.2. Hydrology

The seasonal distribution of discharge in the Drau isdominated by glacier melt, with minimum flow in winterand a maximum in June/July. At the gauging station ofSachsenburg the drainage basin is 2561.4 km2. Thechannel slope is about 0.0015 m/m, the mean flow is76 m3 s 1, with the 30-year flood being 840 m3 s 1 andthe 100-year flood being c. 1029 m3 s 1. Of itstributaries, the Isel River, draining a large area in thecentral Alpine region, has the greatest influence on thesediment regime. The hydrograph during the monitoringperiod for this study is shown in Fig. 2.

3. Monitoring and modelling methods

3.1. Field monitoring methods

During the EU Life Project from 2001 to 2003 andfor two years after it, annual field measurements wereaccomplished to document and analyse the river

the Drau River at KleblachLind in the south of Austria.

Fig. 2. Hydrograph at Sachsenburg with dates of field measurements during the Life-Project related to the restoration measure at KleblachLindfrom June 2002 to June 2003.


geometry, morphological structures, substrate changesand hydraulic parameters in KleblachLind. The fieldmeasurement methodology is described in detail inMayr (2003) and Schober (2004) and is therefore notdescribed in detail here, although a summary is providedto assist in the understanding of monitoring andmodelling decisions. Surveys of the submerged area,the water line and the area of shallows (b0.5 m depth)were performed by tacheometric survey. A boat with adifferential GPS (Leica System 500) and an echosounder (Atlas Deso 14, 210 kHz, 3) allowed rapidcross section surveys with distances of c.15 m betweeneach profile. This was validated by means of tacheo-metric surveys. Average accuracies were 0.02 m(depending on depth) for z-coordinates, and 0.01 mfor the x- and y-coordinates. Flow velocities anddirections were measured with a 2-D electro-magneticvelocity meter (P-EMS, Delft Hydraulics). Substratedata were acquired for the surface layer, using a boatwith an underwater camera for deep areas, andvolumetric samples for shallow water areas and forgravel bars. An underwater video camera with arecorder on board the boat ensured the exact selectionand documentation of sample areas. The position ofindividual points was determined by simultaneousmeasurement using a differential GPS (Mayr, 2003;Schober, 2004).

These field measurements provide a very good basisfor this study of the application of modelling at differentlevels of complexity in river management. However,

the field flow velocity data are only 2-dimensional, andalthough the GPS and echo sounder data are highquality, the bed elevations between each cross section(separated by about 15 m) are not known. This meansthat the monitoring system and resultant data limit theapplication of complex models. A 3-D model requires3-D flow velocities for each node at the inflow(boundary condition) and detailed topographic infor-mation to solve for the complex 3-D flow close to thebed. However, to evaluate this restoration reach over akilometre scale such detail in the monitored data wasunnecessary to assess river degradation and floodprotection. Thus, the monitoring data define an upperlimit for numerical modelling applications.

3.2. Generating digital elevation models with the fielddata set

For the aims and objectives of this study (to evaluaterestoration measures using models of appropriatecomplexity) data were aggregated from original pointsurveys by means of digital elevation models (DEM).The point data were converted to a continuous formusing an interpolation method (Siska and Hung, 2000).There are different interpolation methods, which cangenerally be divided into deterministic and stochasticapproaches (Keckler, 1995; Dollinger and Strobl, 1996).The advantages of using deterministic methods such asTIN (triangulated irregular network) and IDW (inversedistance weighting) are that one can exactly interpolate

Fig. 3. Results of monitoring. Digital elevation models showing the morphological changes from 2001 to 2005 in the side channel initiated on the leftbank.


the observed coordinates. However, single points dobear great weight and can give a false impression ofdetailed morphological structures. Chaplot et al. (2006)evaluated the performance of these techniques for pointheight data with density values from 4 to 109 points/km2

various for surface areas from micro-plots, hillslopesand catchments. Kriging yielded the best estimations atthe lower sampling densities, and in landscapes withstrong spatial structure, low variation of altitude and lowanisotropy. The field data set of the topographic surveyin this study has sampling densities from 104 to 105

points/km2, a high structural variability (poolriffle,embayment) and a low variation of altitude. ThusKriging was used for generating several DEMs for theanalysis of the morphological changes. General advan-tages of this method are given in Vann et al. (2003).

3.3. Model complexity in relation to restoration aimsand data quality

First, regime theory was used to analyse the overallself-adjustment of the river, particularly the minimumlength of enlargement for self-dynamic development(Hunzinger, 1998) and the estimation of the maximum

river width (Schmautz, 2003) over the restored reach.The necessary sedimentological parameters, such ascharacteristic grain diameters and grain size distributionstatistics, were determined by sieving.

To simulate the more detailed effects of morpholog-ical change, models of appropriate levels of complexitywere used, dependent on the quality of the field data, andin an efficient hierarchical approach. The 1-D modelHEC-RAS (Brunner, 2002) was used to simulate hydrau-lics and sediment transport, combined with its extensionHEC-GeoRAS (Ackerman, 2002) for the preparation ofgeospatial data at the reach scale (2 km). This permittedanalysis of changes in water depth, longitudinal flowvelocities, shear stresses and sediment transport capacity.On the other hand, the 2-D model CCHE2D (NationalCenter for Computational Hydroscience and Engineer-ing) was used to analyse particular morphological units,such as gravel bars, islands, embayments, and therestored sidearm Formann et al., 2004. This is a 2-Ddepth-averaged, unsteady, flow and sediment transportmodel. Turbulent shear stresses are approximated usingBoussinesq's approximation and the turbulent eddy vis-cosity is evaluated using three different closure schemes.The numerical method involves an implicit solution, and

Fig. 4. The mass balance with average sedimentation of 0.10 m for the side-channel and 0.006 m for the main-channel for the total restored section onthe Drau at KleblachLind from 2003 to 2004.


uses the control volume approach and the efficientCCHE2D finite element method (Wang Sam and Hu,1992; Jia and Wang, 2001). This numerical model waschosen to simulate variability in water levels or dis-charge, transverse flow velocities, and shear stresses inareas with morphological variety, and also to verifysediment transport in widened sections. This used fielddata from 1 year before and 3 years after the restora-tion. The 2-D model necessitated attention to numericalstability, computation of dry cells, and the possibility ofmodelling sediment transport. It enabled detailed andaccurate simulation of water levels and longitudinal andtransverse flow velocities, and of the processes arisingfrom the morphological changes in the River Drau. Thissummary indicates that a hierarchical approach to modelcomplexity was adopted, appropriate to the varied com-plexity of the restoration aims and the quality of the fielddata.

4. Results of monitoring and modelling the RiverDrau restoration

4.1. Morphological changes based on the monitored data

The monitoring results document high variability inmorphodynamic processes, especially where the riverhas widened in the initiated left-bank sidearm, in whichwidth increased rapidly due to bank erosion (Fig. 3)after the first month. This resulted from an event whichpeaked at a discharge of c.300 m3 s 1, which occursabout once a year on average, and was attained threetimes in the period 20022003. A maximum dischargeof 400 m3 s 1 occurred in December 2002, which is not

a typical timing (see Section 2). This discharge has areturn period of about 2 years. The entrance of theinitiated sidearm was widened from 22 m to 65 m8 months after its first opening. Then, growing gravelbars deflected the flow and initiated further bank failure.At the entrance to the sidearm the mid-channel barconsists of coarse gravel, in contrast to the sandy point-bar on the right-bank at the downstream end of thesidearm. Much woody debris was deposited in themiddle of the new channel, causing very high variabilityin morphological structure (gravel bars, woody debris,and rifflepool sequences), which created new anddifferent habitats (Unfer et al., 2004). In the hydraulicmodelling, the presence of large woody debris hasbeen accommodated by adjustments of the roughnesscoefficient, not by treating it as an element of the bedtopography.

The mass balance (Fig. 4) illustrates an averagesedimentation of 0.10 m between June 2003 and June2004 in the sidearm, corresponding to a mass balance ofabout +3000 m3 over a length of 500 m. The averagebed elevation change in the main channel over the sameperiod was 0.06 m, implying accumulation ofc.10,000 m3 over 2000 m. These mass balance datashow that river degradation had ceased after restoration,but the annual analyses suggest that river widening hasnot yet reached a dynamic equilibrium (Fig. 5). Datafrom other locations, for comparison, include maximumfine sediment deposition rates of 0.0030.071 m/yr(Lewis and Lewin, 1983), and average sedimentationrates of 0.100.14 m/yr (Erskine et al., 1992). However,field measurements by Hooke (1999) show that the rateof fill in channel sidearms is markedly non-linear, and

Fig. 5. Morphological changes (erosiondeposition) in cross Section 4 from 2002 to 2005 (note the accumulation of the mid-channel gravel bar).

Table 2Overview of prediction methods and processes depending on the stateof the river (Thorne, 1998a,b, modified)

Equilibrium streams(steady-state)

References

Regime theory and power lawapproach

Bray (1982), Hey and Thorne(1986), Julien and Wargadalam(1995), Schmautz (2003)

Extremal hypothesis Bettess and White (1987),Chang (1988a), Yang (1992),Millar and Quick (1997)

Tractive force methods Glover and Florey (1951),Lane (1955), Parker (1979),Ikeda and Izumni (1991),Diplas and Vigilar (1992)

Nonequilibrium streams

Fluvial complexity channelboundary, longitudinalchanges, channel boundariesin near bank zone

Einstein and Li (1958), Van Rijn(1984), Yen (1993), Darby andThorne (1996), Thorne (1998a),Millar (2000), Schmautz (2003)

Bank mechanics bank erosion,weakening, mass failure

Grissinger (1982), Lawler (1993),Darby and Thorne (1996),Hey (1997), Rinaldi et al. (2001),Lawler (2005), Rinaldi andDarby (2005)

Basal endpoint Carson and Kirkby (1972),Thorne (1982, 1998a)

Vegetation effects Kirkby and Morgan (1980), Heyand Thorne (1986), Coppin andRichards (1990), Thorne et al.(1993), Darby and Thorne (1996),Van De Wiel and Darby (2004)


later phases of deposition are much slower. Thusaccretion of gravel bars in the Drau is still continuing,but the deposition rate is likely to reduce in the comingyears. Rapid changes in bank erosion also stopped after1 year, which is similar to the results of river channeladjustment following meander cutoff reported by Hooke(1999). She documented rapid changes in the first 23 years with adjustments of width, bar and rifflemorphology, followed by stabilisation after 48 years.It is expected that the Drau mid-channel bar will enlargeand attach to the left bank, that vertical accretion mayincrease, and that growth of vegetation will occur on thegravel bars. The last process has already been observedas willow seedlings have become established on themedial bars in the restored left-bank sidearm of the Drau(Fig. 3).

Analysis of the DEMs shows that the dominant pro-cess following the Drau restoration has been channelwidening due to massive bank erosion in the initiatedbranch. This process involves both fluvial erosion (e.g.Rodi, 1980; Van Rijn, 1984; Hey, 1997) and bank massfailure (e.g. Thorne, 1982; Pizzuto, 1990). This four-fold widening in some cross sections was unexpected,and the sedimentation of bed material due to side-erosion and bed load input may prove to be a problemfor flood protection (see below, Section 4.3.1 on 1-Dhydrodynamic modelling).

4.2. Applications of regime theory to the River Drau

In this and the following section data are used to testand demonstrate applications of some of the modellingapproaches identified in Table 2 (based on Thorne, 1998a;Darby and Van De Wiel, 2003). The results provideinformation on the achievement of the restoration aims,and should also offer a basis for guidance for riverengineers in future restoration initiatives.

The first approach is to apply regime theory to theassessment of the post-restoration equilibrium channelgeometry. For example, Hunzinger (1998) describesa quantitative criterion for braided river adjustment(Eq. (1)) in which the length of the widened reach must

Fig. 6. Comparison between measured and estimated maximum cross section width (after Schmautz, 2003) for a range of assumed bed material grainsizes.


exceed 2LW,Dim to achieve a dynamic evolution of theriver channel.

LW;Dim BA BK

21 2:81ln 1 F 1

where BA = river width in the enlargement section, BK=river width in the original channel, F represents theratio of kinetic and potential energy in the narrow andthe wide channel (for discharges larger than double thecritical discharge for bed load initial motion, this dependsonly on the ratio BA/BK), LW,Dim = the distance from theenlargement section to the point where the expandingflow meets the rigid boundary of the widening for the firsttime. The calculation of this criterion for the KleblachLind reach resulted in 2LW,Dim=320 m, in comparisonto the restored reach length of 450 m.

Schmautz (2003) uses a procedure for estimatingriver widening in a river section with dynamic sideerosion. Characteristic river parameters are determinedin addition to the regime width and the regime length,based on the grain diameter, widthdepth ratio, slopeand scale. The estimation of the river width is here basedon data from physical laboratory model tests. Fig. 6compares width changes in the reach where the sidearmwas initiated with an estimate after Schmautz (2003).This shows differences of from 8% to 27% between themeasured river width in 2005 and the estimated value,depending on the grain diameter used in the prediction.Given that the sidearm width increased by about 340%from 2002 to 2005, this prediction error is reasonable.

This method was undertaken without assumingvegetation, for cohesion-less banks, and in straightriver reaches. Additional investigations that considerbends, cohesive material, and the effects of vegetation

are also necessary (Schober and Habersack, 2004).Nevertheless, these two examples of regime modellingdemonstrate that such approaches can be used by riverengineers for initial estimates of self-formed adjustmentto equilibrium in contexts with limited data availabilityat the reach scale.

4.3. Modelling river restoration with varying degrees ofcomplexity

4.3.1. 1-D hydrodynamic modellingThe cross sectional scale is the most frequently used

spatial unit in river engineering projects over riverlength scales of 1100 km (Habersack, 1997). Here, 1-Dhydraulic simulation software such as HEC-RAS (USArmy Corps of Engineers) can be used, and is applied tothe River Drau over a length scale of approximately2 km. An overview of the realized restoration measuresat this scale is shown in Fig. 7B. This shows that theupstream reach was markedly widened, with embay-ments developed along the right bank. The next reach wasonly minimally widened, and the riprap was removed.Below this, a small branch at the right river bank was dug,creating two islands. Furthermore a second, 450 m longsidearm on the left river bank was initiated. This resultedin a doubling of the cross sectional width and createdbasic conditions for a development of a dynamic rivermorphology.

Hydraulic processes are simplified in 1-D models,and the detailed effects of local morphological structuresare not considered. However, for evaluation of the waterdepth, velocity changes, shear stresses and sedimenttransport capacity, the calibration and validation of thisreduced complexity model gives acceptable results,and requires simplified field data (cross section profiles,

Fig. 7. Overview of the A) regulated river section in 2001 and B) restored channel with altered widths in 2003 in the KleblachLind reach (1D-model,HEC-RAS Schober, 2004; modified).


inflow characteristics) compared to higher dimension-ality models.

4.3.1.1. Changes of hydraulic parameters. Thechanged cross sections due to river widening have acritical influence on hydraulic parameters. Flow veloc-ities and shear stresses decreased significantly in thewidened sections in comparison to the channelized sec-

Fig. 8. 1-D modelling results of the water level changes at 300 m3 s 1 (the mLind reach.

tions. Differences in mean flow velocity of c. 0.25 ms1

and a decrease of average bed shear stresses of about27 Nm2 were simulated. These imply sedimentation onthe river bed and further reduction of depth. In thewidened sections a water depth of 3.8 m at the meanannual flood (300 m3 s1) was simulated, compared to4.5 m in the regulated channel before restoration (Fig. 8).These results, compared with field data in this reach,

ean annual flood) and given the restoration measures in the Kleblach

Fig. 9. 1-D modelling results of the sediment transport capacity at 300 m3 s 1 (the mean annual flood) before and after the restoration measures in theKleblachLind reach Schober, 2004; modified.


support the view that the effects of restoration could bemodelled satisfactorily.

4.3.1.2. Sediment transport capacity. The particularobjective of reducing river bed degradation after chan-nel widening were analysed by simulating sedimenttransport capacity. The software HEC-RAS allowscalculation of sediment transport capacity on the basis ofdifferent sediment transport formulae. Schober (2004)selected formulae for the simulations (Habersack andLaronne, 2000) appropriate for gravel bed rivers(Gomez and Church, 1989). The aim of the simulationwas to analyse changes in sediment transport capacitydue to river widening, and to judge correlations with themeasured river bed geometry. For this an average bed-load grain diameter was used, based on monitoring byHudetz (1999) and Schober (1999).

Comparison of modelled sediment transport capacitywith measured river bed changes was based on using theMeyer-Peter and Mller (1948) equation. In this case,the empirical process understanding (1-D) is adequatefor the applied model. Hunzinger (2003) illustrated thatsediment transport decreases in relation to increases inthe river bed width. For a given sediment input, theequilibrium slope is steeper in a wider section than in anarrow section. This higher slope in a widened reachaffects the river bed upstream. Schober (2004) showedthat the difference in sediment transport capacity beforeand after restoration measures provides useful informa-tion about longitudinal bed level changes related to

erosion and sedimentation. Furthermore, a decrease insediment transport capacity and resultant river bedaggradation after restoration could be analysed (Fig. 9)satisfactorily, and a reduction of river degradation couldbe predicted with this simple 1-D model.

4.3.2. 2-D hydrodynamic modellingHomogeneous structures, long-term and reach-scale

changes can be modelled well by means of 1-D numer-ical models. However, at the local scale (e.g. inundationareas, river channels with islands, detailed structuresassociated with woody debris, successive changes inwidth involving narrowing and widening, transversecurrents in embayments) the application of 1-D modelsis impossible. To simulate and understand processes innon-homogenous river channels, 2-D numerical modelsmust therefore be used (Gilvear, 1999; Schober, 2004).The 2-D model CCHE2D (National Center for Compu-tational Hydroscience and Engineering) was thereforeapplied to illustrate the specific discharge, shear stressesand the discharge division in particular morphologicalunits (e.g. embayments, sidearms, gravel bars). This wascarried out to analyse the objectives of the restorationas a result of the morphological development after therealized restoration measures, on the basis of the moni-toring data sets from 2002 to 2005.

4.3.2.1. Specific discharge (m2/s1). The specific dis-charge (m2 s1) permits visualisation of characteristicflow paths (Fig. 10), and enables an overview of

Fig. 10. a) 2-D modelling results (CCHE2D) of the specific discharge (m2 s 1) at 420 m3 s 1 (bankfull discharge) and b) air photograph withhighlighted gravel bars.


morphological impacts on flow patterns. A mix of dry,shallow and deep water areas, and pool and rifflesequences, is apparent in the initiated sidearm incomparison to the original channel with its homogeneousbends. Furthermore, changes in wetted areas can bedetermined, to indicate variability in river morphology,and to infer expected ecological boundary conditions andresultant habitats (Jungwirth et al., 2003). Visualisingflow in deep pools also allows early analysis of potentiallysensitive areas where river engineering measures may benecessary in the future. For example, the specificdischarge shows that the right-bank embankment in themain channel is in danger of undercutting. This situation

Fig. 11. 2-D modelling results (CCHE2D) of the shear stresses (Nm 2) at 420the island at the right bank, gravel bars).

has not changed after restoration and demands continuingriver management activity.

4.3.2.2. Shear stresses (N/m2). This visualisation(Fig. 11) shows the variability of shear stress in differentmorphological areas. Low stresses in the lee of the twoislands at the upstream of the section are clearly visible,and are likely to lead to future sedimentation. Sedimen-tation can also be expected in the widened sections whereshear stresses are reduced in comparison to the regulatedsections. High shear stresses occur at the top of theupstream island in Fig. 11 and on the right bank at thebeginning of the right-bank lateral channel, causing

m3 s 1 and sensitive areas with high shear stresses highlighted (top of

Fig. 12. 2-D modelling results (CCHE2D) for the divided flow in the side channel as a percentage of total discharge.


lateral erosion at this island. These calculations have beencarried out for different discharges. Sensitive locationscan be identified from the patterns in the hydraulicparameters, and future morphological changes can beinferred by relating the shear stress distributions to flowcompetence and the availability of mobile sediment.

4.3.2.3. Discharge division. Fig. 12 shows the resultsof 2-D hydrodynamic simulations for different discharges(40 m3 s1, 115 m3 s1, 420 m3 s1). At 420 m3 s1 thesidearm discharge increased significantly (within oneyear by 19% as a percentage of total discharge).However, the flow changes stagnated in the sidearmbetween 2003 and 2004 as the gravel bar grew and thestructural variety increased as a result of woody debris.Between 2004 and 2005 the discharge decreasedrapidly because of sedimentation (Fig. 3). The widerange in the percentage of total flow through thesidearm at different discharges (4%24%) indicates thehigh structural variability, especially important at lowerdischarges and suggesting that there is an improveddiversity of habitat which improves the ecologicalcondition. These results show that the hydraulicchanges can be simulated using a 2-D model, providinginformation about ecological boundary conditionsinfluenced by the restoration measures.

4.3.2.4. Modelling of sediment transport. Sedimenttransport modelling was based on equilibrium bed loadtransport of a uniform grain size, and allows for theeffect of secondary flow on the sediment motion incurved channels. Depth-integrated velocities from thehydrodynamic simulation for each node of the finiteelement mesh were used to determine sedimenttransport. For sediment input, the measured grain sizedistribution (Hudetz, 1999; Schober, 1999) was used.The results of modelling provide qualitative evidence

about transport capacity, but are inconclusive becausethey ignore the effect of lateral sediment supply frombank erosion, a process that has not been given muchattention in the research to date but is clearly veryimportant for the sediment flux and for sedimentation.Complex processes in the initiated sidearm at KleblachLind include bank failure (e.g. Thorne, 1982; Van DeWiel and Darby, 2004), dependent on fluvial erosion(Schmautz, 2003; Rinaldi and Darby, 2005) and basalendpoint control, and these cannot be ignored. Channeldynamics also depend on bank material and vegetation(Darby and Thorne, 1996; Rinaldi et al., 2001), anddetailed models of the effect of river widening need torepresent these fully. Thus, process understandingpresents a second upper limit to modelling (the firstupper limit being detailed topographic information; seeabove). This often limits model ability when applied tonatural river channels (Lane et al., 1999).

5. Discussion and conclusions

The extensive restoration measures implementedwithin the EU-Life Project Auenverbund ObereDrau demonstrated that the combined aims of stoppingriverbed degradation, ensuring flood protection, andimproving the ecological integrity of the river-ecosys-tem (see Jungwirth et al., 2003) are possible (Habersacket al., 2003). To achieve this, it was necessary to modifythe boundary conditions that were preventing self-forming development; for example, removing riprap toinitiate bank erosion. This causes a challenge for riverengineers to determine the required spatial extent formorphological development, and to predict dynamicchannel evolution. Detailed morphological changes atthe local scale are difficult to predict and complexnumerical modelling and extensive and time consumingfield monitoring would be necessary.

Fig. 13. State of the art a) monitoring and b) modelling, concerning different complexity for hydrodynamic, bedload and morphodynamicparameters. Definition of minimum reduced complexity for this case study.


In this connection, Fig. 13 summarizes the state ofthe art of monitoring and modelling systems. Not allof them are efficient for the evaluation of the resto-ration aims. Therefore, models with a reduced level ofcomplexity are necessary. This paper has shown thatsimple analysis (such as regime theory) is applicablefor larger scales including the overall river morphology(e.g. river width, length of minimum river widening).Hydraulic effects due to morphological changes weresufficiently determined by means of 1-D (reach scale)and 2-D (local scale) models. Short-term developmentand significant morphological changes were derivablefrom the 2-D model results. The choice of models mustalways consider the data quality combined with thescale of interest, and applications of numerical modelsdepend on lower and upper limits in relation tocomplexity. The lower limits for different modellingapplications are defined by critical factors to beanalysed. The first upper limit is defined by the fielddata quality, e.g. the quality of topographic informationand boundary conditions (water level, flow velocity,and inflow characteristics). The second upper limit isdefined by the understanding and implementation ofcomplex processes in numerical models.

Fig. 13 shows an overview of the monitoring andmodelling systems (1-D and 2-D) used in this casestudy. The minimum complexity of 1-D and 2-D modelsfor hydrodynamic, bedload transport and morphologicalchange is not fully achieved, especially since 2-Dsimulation of morphodynamic changes has not beenpossible. However, monitoring complex hydrodynamicprocesses is possible (e.g. 3-D flow velocities), implyingthat the complexity of modelling and monitoring do notmatch. 3-D hydrodynamic modelling can be state-of-

the-science, but concerning complex bed load transportand morphological modelling, deficiencies still exist. 2-D modelling of river morphology is possible only to alimited degree, although 2-D monitoring of channelmorphodynamics is feasible (Mosselman, 1995). 2-Dmodelling of bed load transport has recently improvedsignificantly, although bedload transport equations arestill restricted to 1-D approaches. These examplesdemonstrate the need to choose models of an appropri-ate level of complexity for the purpose, scale, dataacquisition methods and process representation.

In summary the following conclusions for the RiverDrau can be derived.

1. River widening measures satisfy restoration aims(flood protection, ecological improvement, andstopping river degradation).

2. Rapid changes decreased after 12 years (bankerosion), but a dynamic equilibrium is not reached inthe restored sections after 45 years, thus morpho-dynamic changes are expected (accretion of gravelbars and vegetation cover).

3. Achievement of the aims can be analysed andevaluated with models of appropriate and reducedcomplexity applicable at different scales.

4. The choice of a numerical model depends on a lowerlimit (complexity of the processes to be analysed)and an upper limit (field data quality and computa-tional and process understanding limitations).

5. Channel equilibrium approaches are sufficient for thefirst general estimation at the reach scale (e.g. riverwidths, necessary widening length for self-formingevolution) and dominant processes should be definedbefore numerical models are used to simulate details.


6. Morphological effects at the reach scale (1 km) canbe determined by 1-D models (e.g. water levels, flowvelocity distribution, shear stress, average sedimen-tation), where there are fewer demands for detailedfield data.

7. For local analysis (500 m10 m) and for predictionsof sensitive hydraulic and morphodynamic process-es, 2-D models with medium requirements for fielddata are necessary.

8. For complex morphological changes and detailedprocess understanding, 3-D approaches have to beused, but highly detailed field data are necessary andcomplex process understanding must be implemen-ted in the models.

9. Further research is necessary both into measuringtechniques (for example, of pore water pressure,bank failure, and the influence of vegetation on bankerosion), and into modelling methods that incorpo-rate data from such methods.

This study has thus emphasized the necessity for, andefficiency of, a hierarchical analysis for river manage-ment by means of which appropriate assessmenttechniques can be integrated with models of appropriatelevels of complexity and dimensionality.

Acknowledgements

We acknowledge financial support from the Ministryof Agriculture, Forestry, Environment and WaterManagement and the Carinthia Water Authority. Wethank Peter Mayr, Michael Beheshti, Hugo Seitz andMarcel Liedermann for their help during field work.

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