concept and application of the usable volume for modelling the physical habitat of riverine...

RIVER RESEARCH AND APPLICATIONS

River. Res. Applic. 23: 545–558 (2007)

Published online in Wiley InterScience

CONCEPT AND APPLICATION OF THE USABLE VOLUME FOR MODELLINGTHE PHYSICAL HABITAT OF RIVERINE ORGANISMS

ANS MOUTON,a* HARALD MEIXNER,b PETER L. M. GOETHALS,a

NIELS DE PAUWa and HELMUT MADERc

a Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, Plateaustraat 22, B-9000 Ghent, Belgiumb Department of Soil Bioengineering and Landscape Construction, University of Natural Resources and Applied Life Sciences Vienna,

Peter-Jordan-Straße 82, A-1190 Vienna, Austriac Department of Water–Atmosphere–Environment, Institute for Water Management, Hydrology and Hydraulic Engineering—IWHW,

University of Applied Life Sciences Vienna, Muthgasse 18, A-1190 Vienna, Austria

(www.interscience.wiley.com) DOI: 10.1002/rra.998

ABSTRACT

Most physical habitat models such as PHABSIM (Physical HABitat SIMulation) calculate the Usable Area (UA) of the riversurface or the riverbed as an indicator for the available habitat for a species of interest. Although these models have been widelyapplied, the three dimensionality of habitat hydraulics is increasingly being recognized as an essential issue for understandingthe ecological needs of aquatic organisms. This paper describes the modular toolbox HaMoSOFT (Habitat ModellingSOFTware) which quantifies the available habitat as the Usable Volume (UV) for each class of depth, flow velocity, substrateand cover and for all possible class combinations. The toolbox was calibrated in an artificial river under laboratory conditions.Optimal grids for field sampling were defined by comparing model results based on reduced sampling densities. The UVof anatural river stretch, the Schwechat River in Austria, was analysed at different discharges to assess the impact of flow changes onmodel outputs. UA and UV percentages were modelled for five cross-sectional datasets, one collected in the artificial river, one inthe Zwalm River (Belgium) and three in the Schwechat River at different discharges. UA percentages appeared to be higher thanthose of UV for lower flow velocity classes in all natural rivers and for lower depth classes in all rivers. Analysis of the UsableVolumes for the three Schwechat River scenarios revealed the observed trends were independent of flow changes. The resultsindicate HaMoSOFT is an interesting tool to overcome some shortcomings of Weighted Usable Area (WUA) modelling.Since the UV method is a trade-off between the practical needs for model application in river management and thecomplexity of the three-dimensional hydraulic models, it may be a practical approach for realistic physical habitatquantification. Copyright # 2007 John Wiley & Sons, Ltd.

key words: 3D physical habitat modelling; (Weighted) Usable Volume; (Weighted) Usable Area; PHABSIM; fish; rivers; streams

Received 18 December 2006; Accepted 21 December 2006

INTRODUCTION

Each aquatic organism is adapted to a particular habitat, or a limited number of different habitats, which is reflected

by its specific habitat preferences (Gorman and Karr, 1978; Statzner et al., 1988; Poff and Allan, 1995). In natural

rivers, this habitat segregation is considered the most important factor for resource partitioning (Schoener, 1974)

and thereby allows the coexistence of species and also size classes (Heggenes et al., 2002). The majority of natural

rivers consist of several physical habitat patches, which are characterized by depth, flow velocity, substrate particle

size and in some cases, the amount and type of cover (Heggenes and Saltveit, 1990; Parasiewicz and Dunbar, 2001).

A lot of research has been conducted on the relation between fish presence and these physical microhabitat features

(Armstrong et al., 2003).

Physical habitat assessment in rivers is often used to analyse the impact of different management options, for

instance river restoration on target fish communities (Maddock, 1999). This resulted in the development of several

*Correspondence to: Ans Mouton, Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, Plateaustraat 22, B-9000Ghent, Belgium. E-mail: [email protected]

Copyright # 2007 John Wiley & Sons, Ltd.

546 A. MOUTON ET AL.

physical habitat models (Jowett, 1997; Parasiewicz and Dunbar, 2001; Tharme, 2003). The most common

approaches, such as PHABSIM (Physical HABitat SIMulation) (e.g. Bovee, 1982; Elliot et al., 1999), combine the

Usable (habitat) Area (UA) with Habitat Suitability Indices (HSIs) (Gore and Nestler, 1988) in order to define an

area of habitat suitable for the species of interest, the Weighted Usable Area (WUA) (Waters, 1976). The UA is

hereby based on two-dimensional (2D) distributions of habitat features in the horizontal plane of the river surface or

riverbed. A great deal of research has been done on the application and adaptation of these physical habitat

modelling approaches (Capra et al., 1995; Boudreau et al., 1996; Heggenes et al., 1996; Jorde, 1996; Parasiewicz

and Dunbar, 2001; Mouton et al., in press).

Several aspects of these approaches have been criticized since the 1980s and numerous specific modelling

applications have demonstrated some improvement (Acreman and Dunbar, 2004). Many authors suggested that

microhabitat use of stream fish, and hence the HSIs of these fish, vary at different spatial and temporal scales (Gore

and Nestler, 1988; Vismara et al., 2001; Acreman and Dunbar, 2004; Vilizzi et al., 2004; Moir et al., 2005; Vilizzi

and Copp, 2005). Other critique considers the representation of the three-dimensional flow environment (Mader

and Laaha, 1998). Since flow velocity changes with depth and consists of different values for each point in the river,

all these pseudo 2D approaches use the depth-averaged flow velocity as an estimation of flow velocity at a single

point. However, the use of mean velocities to define the fluvial habitat can be misleading as two points may have

similar depth-averaged flow velocities, but sharply contrasting velocity profiles (Beebe, 1996; Stalnaker et al.,

1996; Mader and Laaha, 1999).

Increasingly, the three dimensionality of habitat hydraulics is being recognized as an essential issue for

understanding the ecological needs of fish (Ghanem et al., 1996; Bremset and Berg, 1999; Newson and Newson,

2000; Rhoads et al., 2003). Several authors have suggested that particular water depths are preferred habitats for

specific fish species or life stages (Greenberg et al., 1996; Heggenes, 1996; Bremset and Berg, 1999). Others

indicate the importance of flow velocity at different depths (Kemp et al., 2003). New methods to quantify physical

habitat features have been published (Nestler and Sutton, 2000; Mader et al., 2003), while greater hydraulic process

representation may be achieved using 2D and 3D hydrodynamic models (Alfredsen et al., 1997; Addley and Hardy,

2002; Booker, 2003; Pasternack et al., 2004; Stewart et al., 2005). These models quantify flow velocities at different

depths, allowing linkage with bioenergetic models (Rosenfeld, 2003; Booker et al., 2004). Yet, few studies have

defined the physical habitat as a combination of different habitat features based on a three-dimensional

characterization of the flow environment.

This paper describes the formulation and testing of a physical habitat model which, instead of the UA, calculates

the Usable Volume (UV), which is the volume of usable habitat for a species of interest (Mader et al., 2005). The

aim was to calibrate and validate the presented model in an artificial river and to define optimal sampling densities

as a trade-off between good model results and sampling efforts. The impact of flow changes on UV has been

evaluated for different flows in a natural lowland river. Finally, the newUV calculation method and the standard UA

method have been compared for the artificial and two natural river stretches. It is expected that the results support

the development of a practical and realistic physical habitat quantification.

MATERIALS AND METHODS

Data collection

Since this paper focuses on the comparison of standard UA and UV, only two physical habitat features, flow

velocity and depth, are being considered. Substrate size and cover type can be neglected as the combination with

these features does not influence the UA and UV values. The presented model takes substrate size classes and cover

types into consideration when WUA and Weighted Usable Volume (WUV) are calculated. The habitat suitability

issue will, however, not be discussed in this paper. Furthermore, the size and distribution of the substrate is similar

in all the three river stretches of interest, ranging between fine and coarse gravel. River geometry of the Laboratory

Creek (LC) was assessed as described by Jugovic et al. (2004) with an optical distance sensor, the SICK-DME 2000

(SICK Vertriebs-GmbH, Dusseldorf, Germany), while for the field sites, a Leica TC 850L Total Station (Leica

Geosystems AG, Heerbrugg, Switzerland) was used. Flow velocity was measured by a 3D Acoustic Doppler

Velocity (ADV) meter at 10Hz. All data were collected at different points in cross-sections (CSs), flow velocities at

each point being measured at different depths.

Copyright # 2007 John Wiley & Sons, Ltd. River. Res. Applic. 23: 545–558 (2007)

DOI: 10.1002/rra

CONCEPT AND APPLICATION OF USABLE VOLUME 547

Study sites

Laboratory Creek. The physical habitat modelling approach presented in this paper has been calibrated and

validated based on data collected in an artificial river, the Laboratory Creek. This is a real scale model (1:1) of a

20m long stretch of the Myra brook, situated in a lowland area of Austria. The model is housed at the Institute for

Water Management, Hydrology and Hydraulic Engineering (IWHW) in Vienna. The width of the stretch ranges

between 2.5 and 4.0m, while depth varies between 0.2 and 0.9m. A detailed description of the artificial stretch and

its construction is given by Mayr et al. (2002).

The flow (0.200m3 s�1) and geometry of this stretch are constant, which allows intensive data collection under

laboratory conditions. Hence, this site is considered as the reference situation in which the real velocity distribution

is estimated most accurately. In order to define optimal sampling grids for field sampling, UV is calculated for

different reduced sampling densities, either by reducing the number of cross-sections, the number of points

measured per CS, or a combination of both. Eight different scenarios were analysed and compared to UV of the

reference scenario, UVref (Table I). In scenarios 2 to 5, the impact of a reduction in the number of CSs measured on

the UV is evaluated, while comparison of scenarios 6 to 10 with scenarios 1 to 5, respectively, reveals the effect of

decreasing the number of points measured per CS. This number decreases by removing each second flow velocity

vertical in every CS, starting from the left bank.

Field sites. Field data were collected from two lowland river stretches, situated in the Schwechat River in

Austria and the Zwalm River in Flanders, Belgium. Both stretches show high river status and hence are

characterized by heterogeneous habitat structures. Cover types and substrate size are similar for both rivers,

the latter ranging from fine to coarse gravel. As the physical habitat of the Schwechat stretch was assessed at three

different discharges, the total field dataset consisted of four different subsets (Table II).

Physical habitat modelling with the HaMoSOFT toolbox

The UV approach is implemented in a modular VBA (Visual Basic for Applications) toolbox called

HaMoSOFT—Habitat Modelling SOFTware (Meixner, 2005). This habitat model consists of three modules

allowing data preparation (Data Preparation Module or DPM), data processing (Data Calculation Module or DCM)

and analysis of the model outputs (Data Analysis Module or DAM). The DCM consists of three sub-modules: the

Data Interpolation Module (DIM), the Equi-Area Calculation Module (EACM) and the Equi-Volume Calculation

Module (EVCM). The toolbox is embedded in Microsoft Excel and uses the program SURFER 7.0 (Golden

Software, Colorado, USA) for data interpolation and visualization.

Data preparation module. In order to adjust the physical habitat model to the study objectives, the user can

choose several settings such as the depth (measured from the river bottom) and velocity classes used for UV

Table I. Description of the different scenarios (S), based on decreasing the sampling density by reducing the number ofcross-sections (CSs; from S2 to S5), the number of points measured per CS (S6), or a combination of both (from S7 to S10)

Scenario (S) Description No. ofCSs

% of availabledata points

Distancebetween CSs (m)

No. ofgeometrypoints

No. offlow velocity

points

S1 Reference scenario 13 100 1.03 561 1026S2 Each second CS is used 7 56 2.04 305 586S3 Each third CS is used 5 39 3.05 217 400S4 Each fourth CS is used 4 31 4.03 173 317S5 The first, last and middle CS are used 3 23 6.07 129 237S6 S1 with reduced data density in CS 13 68 1.03 561 521S7 S2 with reduced data density in CS 7 38 2.04 305 296S8 S3 with reduced data density in CS 5 26 3.05 217 202S9 S4 with reduced data density in CS 4 21 4.03 173 161S10 S5 with reduced data density in CS 3 16 6.07 129 120

Each scenario is characterized by a specific number of CSs included, a percentage of available data points used, the distance between twoneighbouring CSs, a number of geometry points and a number of flow velocity points.


DOI: 10.1002/rra

Table II. Characteristics of data collected in the artificial river, Laboratory Creek and at two field sites, a stretch of theSchwechat River in Austria and a stretch of the Zwalm River in Flanders, Belgium

Site Length (m) Averagewidth (m)

Depthrange (m)

Flow(m3 s�1)

No. ofCSs

Distancebetween CSs

No. ofgeometry points

No. of flowvelocity points

Laboratory Creek 20 3.22 0.2–0.9 0.200 13 1.03 561 1026Schwechat Q1 30 7.31 0.2–1 0.380 15 2.12 123 406Schwechat Q2 30 7.22 0.2–1 0.260 15 2.12 228 400Schwechat Q3 30 7.48 0.2–1 0.390 15 2.12 244 434Zwalm 55 3.50 0.1–0.5 0.45 14 5 192 406

The field dataset consists of four subsets as data at the Schwechat stretch were collected at three different moments (CS¼ cross-section).


calculation, the interpolation method and the resolution of the interpolation grid. The latter was set at 0.01m for this

research. Furthermore, the preferred feature combination can be selected out of all possible combinations of depth,

flow velocity, substrate size and cover classes. Only flow velocity and depth classes have been considered in this

research, the respective width of each class being 0.1m s�1 and 0.1m. Depth classes were measured from the

riverbed to the surface. The module can handle cross-sectional field data, randomly sampled data (Mayr et al.,

2002) and data created by 3D hydrodynamic models such as SSIIM (Sediment Simulation in Intakes with

Multiblock option; Olsen, 2002) outputs. Based on the chosen depth classes, a cross-sectional data structure is

generated which is used as an input for the DCM. In this paper, only cross-sectional raw data from field observations

are considered. The random data processing has been developed in co-operation with the Department of

Mathematical Analysis and Applied Mathematics of Palacky University, Olomouc. A sub-module for using 3D

hydrodynamic model outputs of SSIIM (Olsen, 2002) as HaMoSOFT inputs was developed in cooperation with the

Center for Ecohydraulics Research of the University of Idaho at Boise (Jorde and Yi, 2006).

Data calculation module.

Data interpolation module. Input data for UV calculation, such as flow velocity, substrate size, depth and

cover values, have to be structured in a regular grid. As in most cases X, Y and Z co-ordinates of collected data

points are not regularly structured, a grid of data points is formed by cross-sectional interpolation. Three different

interpolation methods are available: triangulation with linear interpolation, universal kriging with translation

invariable drift and multi-quadratic radial basis functions (Mader and Laaha, 1999). Anisotropy of input data is not

yet considered in this module, which can affect interpolation accuracy when the distance between cross-sections is

large compared to the river width.

Equi-area calculation module for cross-sections. Before modelling the UV, the Usable cross-section

Area UACS is computed in every cross-section, based on the interpolated habitat feature values. The EACM can

handle up to four different habitat features simultaneously: depth, substrate, cover and flow velocity, the latter

resulting in equi-velocity areas within a cross-section. For each combination between the classes of physical habitat

features, the area to which this combination applies is calculated, which is the UA for this combination. This UACS

belongs to the vertical cross-sectional plane and differs from the standard UA lying in the horizontal plane of the

river surface or the riverbed (Bovee, 1982). All UACS values of each cross-section are stored in an

(nþ 1)-dimensional matrix UACS,mat, n being the number of different features chosen in the DPM. For instance,

when all available habitat features are chosen in the DPM, n amounts to 4 and UACS,mat is a five-dimensional

matrix. The total UACS of cross-section m, UACS,m, is calculated by summation over all possible feature classes

(Equation (2)).

UACS;matði; j; k; l;mÞ ¼ UACS;i;j;k;l;m (1)

with UACS,i,j,k,l,m ¼Usable Area of the cross-sectional habitat which belongs to depth class i, flow velocity class j,

substrate class k, cover class l and cross-section m.

UACS;m ¼Xp

i¼1

Xq

j¼1

Xr

k¼1

Xs

l¼1

UACS;i;j;k;l;m

! ! !(2)


DOI: 10.1002/rra


with UACS,m¼Total Usable Area of cross-section m;

p¼ number of depth classes;

q¼ number of equi-flow velocity classes;

r¼ number of substrate classes and

s¼ number of cover classes.

Equi-volume calculation module. For each combination (i, j, k, l) of habitat feature classes, the UV between

two cross-sections m and mþ 1, UVi,j,k,l,m!mþ 1 is calculated as in Equation (3):

UVi;j;k;l;m!mþ1 ¼UACS;i;j;k;l;m þ UACS;i;j;k;l;mþ1

2

� �� sLB;m!mþ1 � sRB;m!mþ1

2

� �(3)

with sLB,m!mþ 1¼ the distance between cross-section m and m þ 1 measured along the left bank (LB) and

sRB,m!mþ 1¼ the distance between cross-section m and mþ 1 measured along the right bank (RB).

Data analysis module. This module generates the standard UA and UV (in m2 or m3 and percentage of

respectively the total UA and UV) for each combination of habitat feature classes. Furthermore, graphs comparing

standard UA and UV for the classes of a chosen feature are created. The WUA and WUV can be calculated by

combining, respectively, the standard UAs and UVs with HSIs of the species of interest. The DSM is developed in

co-operation with the Center for Ecohydraulics Research of the University of Idaho at Boise. This paper is not

focusing on this part of the research as it is similar to the standard WUA approach of PHABSIM (Bovee, 1982).

Comparison of model results

In order to compare the UV with the standard, horizontal UA method, this horizontal UA can be computed in the

Standard Method Equi-Area Calculation Module (EACM-SM). This sub-module converts the flow velocity values

of each velocity profile into the depth-averaged flow velocity. Horizontal interpolation between cross-sections leads

to equi-velocity areas, while overlay of different habitat feature layers (e.g. depth structure and depth-averaged flow

velocity distribution) results in the horizontal standard UA for each combination between the classes of the chosen

habitat features (Bovee, 1982).

Two criteria were used to compare different data sampling scenarios and the results for the five data collections.

The Total Proportional Difference (TPD), ranging from 0 to 200%, is defined as the sum of the absolute difference

in percentage between two UV percentages of one feature class (Equation (4)).

TPDS1$S2 ¼Xk

i¼1

UV1;i � UV2;i

� �� (4)

with TPDS1$ S2¼ the TPD between scenario 1 (S1) and scenario 2 (S2);

UV1,i¼ the UV of habitat feature class i for scenario 1;

UV2,i¼ the UV of habitat feature class i for scenario 2 and

k¼ the total number of classes of the considered habitat feature.

Pearson’s Chi-squared (x2) tests were used to test whether the calculated UV percentages of a specific scenario

were significantly approximating the UV percentages of a reference situation, which are assumed to be a good

estimate of the real UV percentages. If no reference situation was available, the correlation coefficient R2 was used

to indicate the reliability of an observed difference. The more the difference caused by outliers, the lower the R2.

RESULTS

Model calibration and definition of optimal sampling grids

Themodel was calibrated based on data collected in the LC to defineminimal sampling grids needed for accurate

UV calculation. Depending on the amount of data used for physical habitat modelling of the LC, 10 different

scenarios (S1 to S10) were analysed. The HaMoSOFT toolbox generated cross-sectional equi-velocity contour


DOI: 10.1002/rra

0.00 0.50 1.00 1.50 2.00 2.50

0.91.01.1

0.00 0.50 1.00 1.50 2.00 2.50

0.91.01.1

0.00 0.50 1.00 1.50 2.00 2.50

0.91.01.1

0.00 m/s

0.10 m/s

0.20 m/s

0.30 m/s

0.40 m/s

0.50 m/s

0.60 m/s

0.70 m/s

0.80 m/s

0.90 m/s

1.00 m/sCS 4

CS 5

CS6

0.00 0.50 1.00 1.50 2.00 2.50

0.91.01.1

0.00 0.50 1.00 1.50 2.00 2.50

0.91.01.1

0.00 0.50 1.00 1.50 2.00 2.50

0.91.01.1

Distance from left bank (m)






Dep

th (

m)

Dep

th (

m)

Dep

th (

m)

Scenario 1 Scenario 5

Figure 1. Cross-sectional equi-velocity contour plots for scenario 1 (S1; all data points used) and scenario 5 (S5; only 23% of data points used)of cross-sections (CS) 4, 5 and 6 of the Laboratory Creek (Q¼ 0.200m3 s�1). This figure is available in colour online at www.interscience.

wiley.com/journal/rra


plots for each scenario. Visual analysis of these plots already indicated accuracy loss by data density reduction

(Figure 1). The UV percentage for each flow velocity and depth class (Table III), generated according to the

different scenarios (S2 to S10), was compared with the UV percentage of the reference scenario S1, UVref. The UV

percentages for all flow velocity classes were in agreement with UVref. This indicates that the lowest sampling

grids, considered in this paper, are still sufficient to obtain accurate UV simulations of flow velocity classes. For all

depth classes, a significant difference of UV with the UVref was found for S4 and S5, which is illustrated for each

separate depth class in respective Figures 2 and 3. The reason for these significant differences between the reference

scenario (S1, all available input data are used) and the scenarios S4 and S5 is the increasing reduction of the input

data, as shown in Table I (S1¼ 100%, S4¼ 31% and S5¼ 23% of available input data are used).

Table III. Total Proportional Difference (TPD) and p value of the x2 test based on comparison of the UVof scenario 2 (S2) to10 (S10) with UVref (S1) for all depth and flow velocity classes in the Laboratory Creek

Scenario TPD (%) p value of x2 test

UV of low velocity classesS2 13.7 0.9570S3 12.7 0.9317S4 19.3 0.7469S5 22.7 0.4761S6 13.0 0.8885S7 19.3 0.5893S8 14.9 0.7911S9 25.5 0.2507S10 21.0 0.5711

UV of depth classesS2 12.9 0.8194S3 28.4 0.0555S4 27.7 0.0003S5 59.0 1.07� 10�9

As the UV values calculated for each depth class are not influenced by reduction of intra-cross-sectional sampling density (from S6 to S10), thesevalues are equal to the ones from respective S2 to S5 and hence omitted.


DOI: 10.1002/rra

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9

Depth class

Usa

ble

Vo

lum

e (%

)

Scenario 1

Scenario 4

Figure 2. Comparison of UV percentage generated according to scenario 4 with UVref (scenario 1) for all depth classes in the Laboratory Creek(Q¼ 0.200m3 s�1). Depth classes range from 1 (0–0.1m) to 9 (0.8–0.9m)


Model validation in natural situation

The impact of flow changes on the UV of all flow velocity and depth classes is, respectively, illustrated in

Figures 4 and 5. The UVof the different flow velocity classes was similar along the presented flow range. Yet, the

TPD of the two similar flow classes, Q1 and Q3, appeared to be lower than the TPD of any other combination of flow

classes. The reliability of these findings is supported by the high R2 values (Table IV).

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9

Depthclass

Usa

ble

Vo

lum

e (%

)

Scenario 1

Scenario 5

Figure 3. Comparison of UV percentage generated according to scenario 5 with UVref (scenario 1) for all depth classes in the Laboratory Creek(Q¼ 0.200m3 s�1). Depth classes range from 1 (0–0.1m) to 9 (0.8–0.9m)


DOI: 10.1002/rra

Figure 4. Comparison of the Usable Volume (UV) percentage for all flow velocity classes at different flows (Q1¼ 0.380m3 s�1,Q2¼ 0.260m3 s�1 and Q3¼ 0.390m3 s�1) of the Schwechat River. Flow velocity classes range from 1 (0–0.1m s�1) to 11 (1.0–1.1m s�1)


Concerning the UVof different depth classes, the lowest TPD is observed between Q2 and Q3. The highest TPD is

found by comparing UV percentages of the two similar flow classes, Q1 and Q3. This inconsistency in geometry

might be outlier-based as low R2 values were found (Table IV).

Comparison of UA–UV

The modelled UV percentages were compared to the standard UA percentages for the five collected datasets and

for both flow velocity and depth classes (Table V). The p values of the x2 test were calculated assuming that the UV

Figure 5. Comparison of the Usable Volume (UV) percentage for all depth classes at different flows (Q1¼ 0.380m3 s�1, Q2¼ 0.260m3 s�1 andQ3¼ 0.390m3 s�1) of the Schwechat River. Depth classes range from 1 (0–0.1m) to 11 (1.0–1.1m)


DOI: 10.1002/rra

Table IV. Total Proportional Difference (TPD) and R2 based on two by two comparison of Usable Volume (UV) percentages ofdepth and flow velocity classes at three different flows (Q1¼ 0.380m3 s�1, Q2¼ 0.260m3 s�1 and Q3¼ 0.390m3 s�1)

Compared flows TPD (%) R2

UV of flow velocity classesQ1–Q2 26.33 0.9333Q1–Q3 12.84 0.9718Q2–Q3 14.53 0.9881

UV of depth classesQ1–Q2 28.66 0.8267Q1–Q3 30.08 0.7520Q2–Q3 20.06 0.8738


percentages were the reference situation. Standard UA percentages differed significantly from UV percentages of

flow velocity classes only for the LC and for the Schwechat River at Q3, while UV and UA percentages of depth

classes appeared to differ significantly for all considered river stretches.

Visualization of UVand standard UA percentages for all flow velocity and depth classes revealed that the UA of

the lowest flow velocity class was lower than the UV for all natural stretches. The same relation holds for the highest

flow velocity classes of all river stretches, although the observed proportional difference for these classes was small.

The UA percentages for the lower depth classes were exceeding UV percentages for all five stretches, whereas UV

percentages exceeded UA percentages for the highest depth classes. This is illustrated for all flow velocity and

depth classes of the Zwalm River stretch in Figures 6 and 7. Analysis of the model outputs for the Schwechat River

at three different discharges revealed that the observed trends were independent of flow changes. Yet, this

flow-independence was not observed for the TPD between UV and UA percentages.

DISCUSSION

The optimal sampling grid for accurate UV modelling was defined by analysing different scenarios with the

HaMoSOFT toolbox. The UV percentages of all flow velocity classes were not significantly different for the

analysed scenarios. Hence, the minimal sampling grid required for accurate UV modelling of flow velocity classes

might be lower than the densities of the grid discussed in this paper. Concerning accurate UV simulation of all depth

classes, the minimum sampling grid required is given by S3. Yet, these results might be highly dependent on the

sampling grid of the reference situation and on the heterogeneity of the physical habitat in the considered river

stretch. Further research should indicate whether these suggested sampling grids for flow velocity and depth also

hold for other river types or longer river stretches. Moreover, the p value of the x2 test is very sensitive to outliers.

Data reduction by decreasing the number of CSs could coincidently assign a high weight to a CS with extremely

high depth values, although this CS is only representative for a small part of the studied stretch. Hence, random

selection of CSs or bootstrap analysis (Gard, 2005) might lead to different model results.

Table V. Total Proportional Difference (TPD) of the UV percentages and p values of the x2 test for all depth and flow velocityclasses of the five collected datasets

Collected dataset Flow velocity Depth

TPD (%) p TPD (%) p

Laboratory Creek 26.6 0.0441 49.7 6.95� 10�27

Schwechat, Q1 23.3 0.6110 42.1 3.00� 10�9

Schwechat, Q2 32.1 0.2220 40.6 0.0002Schwechat, Q3 37.6 0.0277 48.1 2.48� 10�10

Zwalm 29.4 0.1912 38.7 1.03� 10�14


DOI: 10.1002/rra

0.0

5.0

10.0

15.0

20.0

25.0

1 2 3 4 5 6 7 8 9 10 11 12

Flow velocity class

Usa

ble

Vol

ume

- U

sabl

e A

rea

(%)

Usable Volume

Usable Area

Figure 6. Visualization of UVand standard UA percentages for all flow velocity classes in the Zwalm River stretch. Flow velocity classes rangefrom 1 (0–0.1m s�1) to 12 (1.0–1.2m s�1)

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

1 2 3 4 5 6

Depth class

Usa

ble

Vol

ume

- U

sabl

e A

rea

(%)

Usable Volume

Usable Area

Figure 7. Visualization of UV and standard UA percentages for all depth classes in the Zwalm River stretch. Depth classes range from 1(0–0.1m) to 6 (0.5–0.6m)


DOI: 10.1002/rra



Flow changes had a significant impact on UV calculation of both flow velocity and depth classes. For the velocity

classes, this effect was consistent with the differences in flow, the TPD of the two similar flows (Q1 and Q3) being

the lowest. Surprisingly, opposite results were observed for the impact of flow on the UVof depth classes as the TPD

of the similar flow situations was higher than the TPD of any other flow combination. This finding could be due to

errors in the collected data or to geometry changes in the time interval between the two measuring times (e.g. bed

transportation, change in dead woody debris, etc.).

Comparison of the standard UA percentages to the UVmethod for flow velocity classes only revealed significant

differences between UV and UA percentages in two out of five river settings. Yet, although both calculations are

based on the same datasets, non-zero TPDs were observed, indicating a clear difference between UV and UA.

Moreover, the UA percentage of the lowest flow velocity class was lower than the UV percentage for all natural

stretches, whereas UV percentages exceeded UA percentages for the highest flow velocity classes. The latter might

be due to the loss of extremely high velocity values by depth averaging of flow velocity. Similar relations were not

observed for extremely low velocity values as the flow velocity distribution was skewed towards the lower values.

The information loss by depth-averaging measured or simulated velocity data might hamper model accuracy. Many

authors observed an impact of local flow velocity or snout velocity on habitat suitability of Atlantic salmon (Salmo

salar L.) and brown trout (Salmo trutta L.) in streams (Armstrong et al., 2003; Kemp et al., 2003).

In all river settings TPD between standard UA and UV percentages for depth classes turned out to be significant.

The UV percentages for the highest depth classes were higher than UA percentages for all five stretches, whereas

UA percentages exceeded UV percentages for the lower depth classes. Considering a regular flow gauge with

trapezoidal cross-sections, a design which is approximating many river geometries, UA percentages exceeded UV

percentages of all depth classes i for which i< n/C, n being the total number of depth classes and C being a constant,

depending on the cross-sectional shape of the river. The underestimation of usable habitat quantity for the highest

depth classes by the standard UA approach can be explained analogously. Substrate size classes and cover type

classes are similar to depth classes as they are also delimited by vertical borders. Hence, the relations between

standard UA and UV percentages of substrate size and cover type classes will be similar to those between UA and

UV percentages of depth classes and have not been considered in this paper.

Many authors describe the impact of depth, cover and substrate on the habitat suitability for specific fish species

or life stages (Rosenfeld and Hatfield, 2006). A great part of this research focuses on the two salmonid species:

Atlantic salmon and brown trout (Bremset and Berg, 1999; Armstrong et al., 2003). Heggenes (1996) reports

young Atlantic salmon often prefer shallower areas, whereas brown trout chose deep stream areas with rocky

substrates. According to bivariate habitat suitability models for brown trout based on depth and flow velocity data,

depth appears to be much more important than velocity in defining habitat suitability requirements (Vismara et al.,

2001). Moreover, water depth can be a critical factor for habitat connectivity and hence influence fish migration

(Heggenes et al., 1996).

The results indicate that most added value of the UV approach compared to the standard UA method lies in its

detailed description of the UV percentage of different flow velocity, depth, substrate size and cover type classes.

When physical habitat models do not operate on a spatial scale which is relevant to fish, they may be unreliable and

could lead to unfounded management decisions (Heggenes, 1996). Hence, the UV approach may overcome this

danger. Yet, further evaluation of model results (Gard, 2005) and model optimization are needed. Spatial

semi-variance approaches could be used for comparison between field data and model output to incorporate

important spatial characteristics of model performance (Clifford et al., 2005).

Future research should focus on other habitat features influencing habitat suitability of aquatic species.

Incorporation of biotic interactions (e.g. predation, competition, etc.; Copp et al., 2005) and bioenergetic

aspects would improve model accuracy (Booker et al., 2004). Temporal effects such as water quality fluctuations

and seasonal changes of flow and temperature induce changes in habitat use and preference of fish

(Maki-Petays et al., 1997; Vehanen et al., 2000). Hence, these effects and their interactions should be merged in a

general habitat model by linking models which describe different aspects of the aquatic habitat, such as chemical,

physical and biological features. The HaMoSOFT toolbox presented in this paper could be part of such a

habitat model. The UV can be linked with HSIs to define the volume of suitable habitat for a species, the WUV,

while the use of hydraulic simulations (e.g. SSIIM; Olsen, 2002) as model inputs should lead to increasing

model accuracy.


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To conclude, the HaMoSOFT toolbox is an interesting alternative to overcome some shortcomings of the widely

used WUA modelling as it can combine different physical habitat features and HSIs into the WUV for a species of

interest. Since the UV method is a trade-off between the complexity of the 3D hydraulic models and the practical

needs for model application in river management, it may be a practical and realistic approach for physical habitat

quantification.

ACKNOWLEDGEMENTS

The HaMoSoft project is financed by the Austrian Science Fund (FWF Project No. P16453–N07).We acknowledge

COST Action 626 networking and meeting possibilities for making the collaboration possible between Austrian

and Belgian colleagues. Ans Mouton is a recipient of a Ph.D. grant financed by the Special Research Fund (BOF) of

Ghent University.

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concept and application of the usable volume for modelling the physical habitat of riverine...

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