About the project

Extended project description


Bed roughness in natural water systems has traditionally been studied extensively as it strongly affects water levels (hence, safety during flood events), flood propagation in rivers, propagation of tides, widening or retreat of beaches, the rate and composition of the suspended load and bedload transport, and thus large-scale morphodynamics (including overall aggradation or degradation) of a river or sea bed. A better insight in bed roughness is crucial for policy making in lowland countries, as The Netherlands, where protection along coastlines and rivers is of vital importance. This research will be useful in future Dutch large-scale infrastructural projects like De Maaswerken, PKB Ruimte voor de Rivier, the measures in the Scheldt estuary and the activities foreseen in the North Sea (such as wind energy parks, aquaculture, sand mining and artificial islands).

Bed roughness

Water in natural water systems – such as rivers, estuaries, seas and oceans – is subject to a number of forces: (1) gravity, acting in downslope direction; (2) atmospheric pressure gradients; (3) wind, which may generate currents and waves; (4) gravitational attraction of astronomical bodies, giving rise to tidal currents; and (5) friction, acting in the opposite direction of the flow. The relation between these forces determines the character of the flow (e.g., uniform or non-uniform, steady or non-steady, laminar or turbulent, and subcritical or supercritical), as well as flow velocities, wave heights, water levels and the flow's ability to transport sediment.

The frictional force acting upon the flow results from roughness elements on the bed. The larger the roughness elements, the steeper the velocity gradient towards the bed and the larger the shear stress acting at the bed (i.e., the boundary shear stress). Figure 1 shows a typical vertical flow velocity profile. The influence of roughness elements on the flow is described in terms of roughness models (e.g., Chézy, 1775; Manning, 1891; Darcy-Weisbach (Taskforce, 1963)), which are also called models of flow resistance, hydraulic roughness, bed resistance, bottom friction or bed friction. For flows that are approximately uniform and steady, these models present a relation between the mean flow velocity and the boundary shear stress, and thus between the mean flow velocity, slope and hydraulic radius.

Sources of roughness

Bed roughness comprises several components: (1) grain roughness, (2) form roughness, (3) resistance due to vegetation, (4) resistance due to channel irregularities, (5) resistance due to suspended load, and (6) resistance due to intergranular forces in intense bedload transport conditions.

Grain roughness is a function of the representative grainsize of the bed material. For non-uniform sediment, the effective grainsize is larger than the mean and is usually somewhat arbitrarily chosen as the D84 or D90, which is the grainsize for which 84 or 90 percent of the mixture is finer. Note that the grain roughness affects sediment transport rates as it is the only part that contributes to the drag force that actually moves the grains.

Form roughness stems from pressure differences over disturbances of the bed (i.e., bedforms, see Figure 2), which arise through sediment transport and instability of the bed. In sand-bed rivers, form roughness often exceeds grain roughness in importance. For gravel-bed rivers, form roughness due to bars is important at lower flow rates, whereas grain roughness is dominant at higher ones (Parker and Peterson, 1980).

Resistance caused by vegetation has received more attention in the last few decades, not in the least because of recent river restoration and rehabilitation projects in which natural riverbanks and floodplain vegetation have been preserved or restored. Resistance due to vegetation can be subdivided into two groups: resistance due to submerged short vegetation and resistance due to non-submerged tall vegetation. Figures 3 and 4 show the experimental set-up of a flume experiment with natural vegetation and a measured flow velocity profile over submerged vegetation, respectively.

Resistance due to channel irregularities and resistance due to suspended load have received relatively little attention. Natural channels are characteristically irregular and considerable energy loss can occur because of local bank irregularities and changes in channel alignment (e.g., Leopold et al., 1960). Suspended material in the flow tends to decrease turbulence, thereby reducing resistance, yet these effects appear to be relatively small (Raudkivi, 1976). Resistance due to intergranular forces is of importance in intense sediment transport conditions (e.g., Dohmen et al., 2001).

Modelling roughness

In analyses of hydraulic-morphological behaviour, the definition of the roughness coefficient for a channel section is an important requirement. This may be achieved by estimating the total bed roughness from measured mean velocity, flow depth and slope. Yet, an objective estimation of the independent controls would be preferable. In practice, the above multiple sources of roughness complicate this approach. Sometimes tabulated values of roughness for representative reaches or photographs with accompanying friction factors (e.g., Simons and Richardson, 1966; see also Figure 2) can be of help. When actually modelling bed roughness, we usually follow the approach introduced by Meyer-Peter and Müller (1948) and divide the bed shear stress τ into a part caused by friction over a rough bed without bedforms (τ') and a part caused by form drag through the presence of bedforms (τ''): τ = τ' + τ''. At very high flow velocities with intense sheet flow, Wilson (1989) distinguishes a third bed shear stress term (τ'''), which is called the sediment transport contribution to the bed shear stress caused by momentum transfer to mobilise the grains. We can solve the total shear stress τ by using submodels predicting the above two (or three) components, using Darcy-Weisbach, Chézy, or Manning friction factors or shear velocities. The Manning and the Darcy-Weisbach friction factors are directly related to bed roughness, whereas Chézy is rather a measure of flow conductance than of resistance. Only the Darcy-Weisbach friction factor is dimensionless.

bedform type



wave length


empirical model

theoretical model

ripples (fluvial)

< 0.04 or

2 to 100 times grain size

< 0.6

Bagnold (1956)

dunes (fluvial)

< 1/3 times flow depth

~ 4 to 8 times flow depth

Allen (1968)

Gill (1971)

Yalin (1971,1977,1992)

Van Rijn (1984)

Engelund (1970)

Fredsøe (1982)

Niño et al. (2002)

antidunes (fluvial)

~ length surface waves

Kennedy (1963)

chutes-pools (fluvial)

alternate bars (fluvial)

~ flow depth

~ > channel width

Struiksma et al (1985)

Colombini et al (1987)

ripples (coastal)



Nielsen (1981)

Grant and Madsen (1982)

Van Rijn (1989)

Mogridge et al. (1994)

Wiberg and Harris (1994)

Blondeaux (1990)

Roos and Blondeaux (2001)

Hansen et al. (2001)

antidunes (coastal)

0.001 – 0.01


megaripples (coastal, offshore)



Soulsby (1983)

bars (coastal)



Plant et al. (1999)

ridges (coastal)



Calvete et al. (2001)

sandwaves (offshore)



Hulscher (1996)

Nemeth & Hulscher (2003)

sand banks (offshore)



Huthnance (1982)

Hulscher et al. (1993)

Table 1 Incomplete list of empirical and theoretical models predicting bedform dimensions. Small bedforms that may occur in gravel-bed rivers (e.g., pebble clusters, transverse ribs, clast dams) are not considered here.

Examples of models of grain roughness for fluvial applications are those by Strickler (1923), Leopold and Wolman (1957), and Van Rijn (1984). Nikuradse (1932) introduced the concept of an equivalent sand roughness for roughness elements protruding into the flow. Existing models of form roughness are mostly based on empirical or theoretical models of bedform dimensions. Table 1 presents an incomplete list of existing models predicting dimensions of various types of bedforms. Form roughness models translate these bedform dimensions into values for bed roughness. Yet, form roughness models can also be based on integral flow parameters (e.g., Engelund and Hansen, 1967). Wright and Parker (2004) propose improvements to the Engelund and Hansen form roughness model for conditions of large low-slope sand-bed rivers, by accounting for density stratification effects.

Problem identification

In hydraulic-morphological models applied in water management studies, bed roughness is mostly used as a calibration factor. This calibration factor may include spatial variation. Yet, in most cases it is simply taken constant, thus neglecting much of the physics involved. We usually simply ignore the effects of the stochastics of bedforms on form roughness, the dynamic character of form roughness during flood events in rivers, the effects of the composition of the river bed surface on grain roughness, biological effects on roughness and estuarine morphology, and the uncertainties playing a role in roughness modelling. This research aims to improve roughness modelling, focusing on Dutch water management practice. Issues that will not be considered in this project are roughness under wave-dominated conditions and the effects of suspended load transport on bed roughness. In other words, we will not focus on near-coastal issues but leave this matter for future research. Below, specific problems in roughness modelling relevant for the research are addressed.

Effects of bedform stochastics on form roughness in rivers, estuaries and seas

Despite the numerous models of bedform dimensions, the important characteristics of bedforms (mean height, mean length, migration speed, variation in these parameters) still cannot be predicted with appropriate accuracy. Besides, models of form roughness are usually based on mean bedform properties, whereas flow, bedforms and roughness usually show large spatial variation and different types of bedforms may be present simultaneously (e.g., Soulsby, 1983).

The spatial variation in bed surface elevation can be considered a stochastic phenomenon (Figure 5), which is not incorporated in the commonly applied form roughness models, yet it appears to affect form roughness significantly. This subject is a focus of Subproject A1 (Van der Mark).

Roughness modelling and large-scale offshore morphodynamics

The sandy bottom of shallow seas as the North Sea is quite complex (see Figure 6). Most of the sea bed is covered by large bed waves like sandwaves and sandbanks. These patterns are free instabilities within the physical system of a sandy sea bed and tidal flow (Hulscher, 1996). They are dynamic: sandwaves migrate with about 10 metres per year and show variations in amplitude and wavelength. Hulscher and Van den Brink (2001) demonstrate that megaripple form roughness – estimated on the basis of local depth and current information – is crucial for the occurrence of sandwaves. At the moment, such spatial variations in roughness are not accounted for. In Subproject A1, effects of bedform occurrence and bedform dynamics will be included in roughness models.

So far, offshore human activity has avoided interfering with sea bed patterns: (a) platforms are positioned outside sandwave fields, (b) pipelines and cables either avoid these areas or are buried in the sea bed at a safe depth, and (c) sand mining is limited to small quantities. However, with demands increasing, it will become more difficult to avoid possible problems related to large bed patterns. For a safe use of the offshore environment, understanding the dynamics of large bed patterns and its consequences for human activities is important.

Van der Veen et al. (2003) postulated a method to account for roughness due to a windmill park itself. Large-scale interferences will influence roughness directly and indirectly, i.e., through bed patterns and hence, form roughness. Neither are currently taken into account. This means that we are unable to predict the consequences of these large-scale offshore human interference for, for instance, the safety of the coast. Subproject C3 (Hulscher) will collect data and combine them with state-of-the-art form roughness models and thus enable such an evaluation.

Dynamic character of form roughness during flood events in rivers

Accurate form roughness modelling is complicated as bedform dimensions and form roughness generally increase with increasing discharge and decrease as the flood recedes (e.g., Julien et al., 2002). Yet, in hydraulic-morphological models, bed roughness is mostly simply used as a spatially varying but constant calibration factor, thus neglecting the dynamic character of bedform development and form roughness. This may lead to inaccurate predictions of water levels.

Moreover, commonly applied roughness models are limited to stationary and uniform conditions, whereas in natural systems flow conditions can strongly vary in time. Yet, bedform development and form roughness show a temporal and spatial lag with respect to a change in flow rate (e.g., Wijbenga, 1990). This means that bedform development and form roughness are not in equilibrium with the prevailing flow conditions and show hysteresis effects with varying discharge (see Figure 7). Actual bedform dimensions therefore depend not only on the present discharge but also on past discharges. Various studies have focussed on how bedform properties and form roughness vary with changes in flow (e.g., Tsujimoto and Nakagawa, 1983), but understanding of the physical processes is still limited. Besides the above temporal lag in bedform development (i.e., form roughness), also the change in grainsize distribution of the bed surface (i.e., grain roughness) due to the break-up of a coarse bed layer during a flood event appears to play a role. Time evolution of form roughness during flood events in rivers is the focus of Subproject C1 (STW Paarlberg).

Effects of the bed surface composition on grain roughness in rivers

Existing models of grain roughness are mostly based on a representative grainsize of the bed material. In situations where the bed material is non-uniform, the representative grainsize of the mixture is larger than the mean, for instance the D84 or D90.

Recently, progress was made in modelling vertical sorting (i.e., the vertical organisation of grain size fractions) in bedform-dominated rivers, and thus the bed surface composition (Figure 8). This improved description of the bed surface composition offers important opportunities for better modelling of the river beds’ grain roughness. Adequate incorporation of the variation in grain size on grain roughness forms the challenge of Subproject A2 (STW-VENI Blom).

Roughness modelling in large-scale morphological river problems

Existing models for bed roughness are generally empirical, developed on the basis of laboratory experiments. Especially Reynolds numbers (on grain scale and bedform scale) are different for sediment/water flows in rivers and in laboratory channels, which gives rise to scale effects that are generally not accounted for. In Subproject C2 (Tuijnder), river data and existing laboratory data of sand-gravel bed roughness will be studied and analysed in a common framework. The project will bring data-based modelling of bed roughness from the level of controlled small-scale laboratory conditions to the (practical) level of full-scale river conditions.

Bed roughness is a central element in the interaction between river flow, sediment transport, large-scale river morphodynamics and grain-size sorting. For example, large-scale bed degradation coinciding with armouring processes affects small-scale bedforms and river bed roughness, and vice versa. The performance of one-dimensional (1D) and two-dimensional (2D) models for large-scale river morphology depends strongly on an adequate submodel for bed roughness. Especially in graded-sediment models, much uncertainty exists about how to model small-scale sediment dynamics, roughness and associated parameters adequately. In Subproject C2, the modelling of roughness is studied in the context of large-scale morphodynamics-related problems in sand-gravel rivers.

Vegetation roughness in river floodplains

Ecology is an important aspect of sustainable river basin management. Due to the dynamics of alternating wet and dry periods, floodplains have a high ecological potential and can form essential ecological networks as they provide long and continuous habitats for plants and animals. The ecological function combines well with the search for increased storage capacity in the river system induced by expected climate change. However, natural landscape development generally increases the roughness of the floodplain causing water levels to rise during floodplain inundation. In recent years, research on vegetation and its effects on bed roughness and turbulence has received more attention (e.g., Stephan and Gutknecht, 2002; Järvelä, 2002; Baptist, 2003). Yet, an appropriate way of taking into account the effects of floodplain vegetation on bed roughness and water levels during flood events is not readily available (Favier and Augustijn, 2002). 'Appropriate' in this context means 'as simple as possible, but reliable enough for the intended purpose'. It is the focus of a PhD research project that recently started within the WEM group (Huthoff). The project’s challenge is to determine an appropriate way of including vegetation roughness in hydraulic models in order to evaluate the effects of natural landscape development plans on river floodplains.

Biological effects on estuarine morphology

Morphology and ecology interact. A number of factors, including flow dynamics and bed characteristics (sand/mud composition), determine the occurrence and abundance of specific species. Biological activity influences the stability of sediments in two ways (Widdows et al., 2000; Knaapen et al., 2003): some organisms, like algae and bacteria, stabilise the sediment by covering it or by excreting substances that cement the sediment; other organisms destabilise by bioturbation and lower the critical shear stress required to initiate sediment transport. The magnitude of the influence of these processes on morphology, however, is not well known and temporal and spatial variation in biological activity further complicates the issue. So far, morphological modelling has paid little attention to this influence, which is highly related to bed roughness. This lack of knowledge significantly hinders the modelling of roughness in estuaries. Subproject A4 (De Vries) of this research focuses on how biological processes influence bed roughness, sediment transport and morphological changes.

Dealing with uncertainty in roughness modelling

The results of hydraulic models are uncertain due to uncertainties in model parameters, boundary conditions and modelling choices. One of the main sources of uncertainty in computed water levels originates from hydraulic roughness of the river bed.

This uncertainty affects the model results in several ways (Van Asselt, 2000). Firstly, roughness as a model parameter varies in time and in space (technical uncertainty), resulting in spatial and temporal variability of the modelled bed levels and water levels (Mosselman et al., 1999). This variability can be described statistically. Secondly, hydraulic roughness can be included in a model in several ways (e.g., as a calibration parameter or through a roughness predictor). The choice of this type of modelling affects the model results (also called methodological uncertainty, due to lack of knowledge), thus inducing an uncertainty in the model results.

Usually, the roughness parameter is applied as a calibration parameter. However, no field measurements are available of the conditions which bed roughness models are most relevant for. This makes calibration of bed roughness models for these conditions simply impossible.

Knowledge of the type and magnitude of uncertainties is crucial for a meaningful interpretation of the model results and their usefulness in decision-making. Research on these uncertainties and quantification of their effect on the model results has started only recently (e.g., see Figure 9). In Subproject B1 (Noordam), the type and magnitude of the uncertainties in roughness modelling and their effect on the model results is evaluated, both for 1D and 2D models.

Integral assessment framework for water management studies

Large infrastructural projects are being carried out and envisaged in a number of water systems in The Netherlands. Examples are the projects De Maaswerken and PKB Ruimte voor de Rivier, the range of measures in the Scheldt estuary and foreseen activities (e.g., wind energy parks, aquaculture, sand mining and artificial islands) in the North Sea. Such projects are part of a larger integrated water management framework, designed to increase protection against flooding, improve environmental quality or stimulate the local or national economy. These initiatives operate within a complex web of interactions between short- and long-term economic costs and benefits, technical feasibility, environmental impact assessments, current (international) policy and management, international law and general public interest. We need an integrated assessment of all relevant aspects involved. Such an assessment framework, founded on an adequate knowledge base, is currently missing. Its development is the focus of Subproject B2 (Hommes). It will incorporate technical, physical, economic, ecological and societal aspects of the feasibility of proposed projects and provide decision makers with vital information.


Ultimate goal of this project is to improve roughness models for better management of rivers, estuaries and seas.

Research questions

The central research question of this research is:

How can we incorporate essential physical (sedimentary, vegetation and biological) influences in roughness models so that water management measures can be sufficiently evaluated in advance?

This central question splits into the following Research Questions:

  1. Which sedimentary and biological factors influence roughness and how can we effectively incorporate these?
    1. How can we account for bedform variations in roughness models?
    2. How can we account for grain size variations in roughness models?
    3. How can we account for vegetation in roughness models?
    4. How can we account for essential biological and morphodynamic feedbacks in roughness models?
  2. Which factors, among which roughness models, hinder evaluating water measures in advance and how can we deal with this?
    1. Which sources of uncertainty affect roughness models? Which sources can be effectively dealt with in a statistical sense? What do we gain by reduction of uncertainty?
    2. From the multidisciplinary viewpoint of decision making, what do we gain by improved roughness models and what other improvements are most promising?
  3. What water management problems benefit from improved roughness models and in which way?
    1. What do flood event modelling and water managers gain by accounting for roughness changes due to variations in dune dimensions during river floods?
    2. How do large-scale river morphology management issues benefit from improved roughness modelling?
    3. Learning from improved roughness modelling, what problems in fluvial, estuarine and offshore water management can gain by improved roughness modelling and which research is necessary to enable that.


The research questions require a multidisciplinary approach so that a combination of several methods is most appropriate. For instance, research question A asks for typical technical approaches, whereas B2 needs methods combining technical and social sciences.

Approach and Subprojects

Each of the Research Questions (RQ) is investigated in subprojects (Table 2). The projects by Paarlberg (STW-TCB.6222) and Blom (STW-VENI BCB.6286) are formally not part of the ROUGH WATER VICI project, but are included here as their topics are strongly related to the VICI subprojects.


Subjects of subprojects








Form roughness

Grain roughness

Vegetation roughness

Bio-morphodynamic roughness

Van der Mark



Van der Mark

De Vries

Van der Mark



Stochastics and uncertainty

Assessment framework for water management








Dynamic roughness during floods

Roughness and large-scale morphology

Water management applications






Table 2. Translation of Research Questions into subprojects and specific water systems to be investigated.


The problem, how to model bed roughness, itself is not new. However, its significance for water management is growing fast for a number of reasons. Firstly, there is a society-driven tendency towards less technical measures (higher dikes) and towards a combination of technical and social measures (e.g., flood emergency basins along the Dutch rivers) and reserving space for nature reserves, so-called ‘living with water’. Secondly, climate change will most probably lead to more extreme weather conditions (for rivers: more extreme droughts and more extreme flood events). This requires more precise quantitative knowledge, also in a statistical sense, for an appropriate evaluation and design of future water management measures. This makes the traditional fluid dynamics problem transform into a very new and demanding topic in water management.


In hydraulic-morphological models, the roughness parameter is usually applied as a calibration parameter. In this project improved roughness modelling will be immediately applied to hydraulic and morphodynamic cases. A physics-based model that describes river dune development and dune behaviour during extreme flood events will be developed and translated into a dynamic roughness model in order to improve prediction of water levels along rivers. Such couplings have not been implemented before.

This project combines the results of innovative modelling in the natural domain (to assess the technical, physical and ecological feasibility of human interferences in water systems) with societal, political and economic aspects in an integrated assessment framework. It aims to improve decision making in integrated water and coastal management by providing more accurate (i.e., appropriate) information and knowledge to policy makers and managers.

Improving roughness modelling from a biological and/or physical point of view will lead to new options for water management, based on these physical mechanisms. A marine example is presented by Roos & Hulscher (2003), who used physical knowledge of seabed dynamics to show that the orientation of an elongated sand pit highly influences the rate at which the seabed surrounding this pit changes. This will help in planning the geometry and location of sand pits.

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