Characterizing and modelling river channel migration rates at a regional scale: Case study of south-east France

Journal of Environmental Management xxx (2016) 1e15

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Research article

Characterizing and modelling river channel migration rates at a regional scale: Case study of south-east France
Pie
gay b Adrien Alber a, *, Herve a b

Direction R
egionale de l'Environnement, de l'Am
enagement et du Logement Centre Val de Loire, 5 Avenue Buffon, 45064 Orl
eans Cedex 2, France University of Lyon, UMR 5600 CNRS EVS, Site ENS, 15 Parvis R. Descartes, BP 7000 69342, Lyon Cedex 07, France

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 May 2016 Received in revised form 15 September 2016 Accepted 26 October 2016 Available online xxx

An increased awareness by river managers of the importance of river channel migration to sediment dynamics, habitat complexity and other ecosystem functions has led to an advance in the science and practice of identifying, protecting or restoring specific erodible corridors across which rivers are free to migrate. One current challenge is the application of these watershed-specific goals at the regional planning scales (e.g., the European Water Framework Directive). This study provides a GIS-based spatial analysis of the channel migration rates at the regional-scale. As a case study, 99 reaches were sampled in ^ ne Basin and nearby tributaries of the Mediterranean Sea (111,300 km2). We the French part of the Rho explored the spatial correlation between the channel migration rate and a set of simple variables (e.g., watershed area, channel slope, stream power, active channel width). We found that the spatial variability of the channel migration rates was primary explained by the gross stream power (R2 ¼ 0.48) and more surprisingly by the active channel width scaled by the watershed area. The relationship between the absolute migration rate and the gross stream power is generally consistent with the published empirical models for freely meandering rivers, whereas it is less significant for the multi-thread reaches. The discussion focused on methodological constraints for a regional-scale modelling of the migration rates, and the interpretation of the empirical models. We hypothesize that the active channel width scaled by the watershed area is a surrogate for the sediment supply which may be a more critical factor than the bank resistance for explaining the regional-scale variability of the migration rates. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Bank erosion rate Regional-scale GIS Modelling Stream power

1. Introduction The channel migration is a geomorphic process of alluvial rivers which has been extensively documented over a range of scales for meandering (Brice, 1974; Hooke, 1995; Bartholdy and Billi, 2002), wandering (O'Connor et al., 2003) and braiding pattern (Hickin and Sichingabula, 1988; Reinfelds and Nanson, 1993). River management has long attempted to systematically stabilize the bank erosion to mitigate the property loss and degradation of in
gay et al., 1997). Fluvial practitioners now frastructures (e.g., Pie recognise the channel migration as a key process to preserve or restore because it strongly influences the structures and functions of aquatic and riverine ecosystems (e.g., Beechie et al., 2006; Florsheim et al., 2008). Guidebooks are being published in different countries to help managers to delineate an erodible

* Corresponding author. E-mail address: [email protected] (A. Alber).

corridor (or channel migration zone) at the reach-scale defined as the space where the river may be free to migrate (e.g. Malavoi et al.,
gay et al., 2005). A better under1998; Rapp and Abbe, 2003; Pie standing of the spatial variability of the channel migration at the regional-scale is now required to plan the preservation or restoration of rivers (Beechie et al., 2006; Hall et al., 2007). Empirical models of the channel migration rates have been developed over a range of scales, from the bend-scale (Ikeda et al., 1981) to the world scale (Hooke, 1980). On the one hand, a set of empirical models show a positive spatial correlation between the absolute migration rates and the discharge, the active channel width or the watershed area used as a surrogate, implying a longitudinal increase at the river or regional-scale (Hooke, 1980; Brice, 1982; Nanson and Hickin, 1986; Nicoll and Hickin, 2010). On the other hand, several authors observed a positive spatial correlation between the absolute migration rate and the gross stream power at the regional-scale (Nanson and Hickin, 1986; Nicoll and Hickin, 2010). Following the terminology of Lawler (1992), the gross stream power (expressed in Watt/m) corresponds to the rate of

http://dx.doi.org/10.1016/j.jenvman.2016.10.055 0301-4797/© 2016 Elsevier Ltd. All rights reserved.


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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2

energy expenditure per unit length of the river and it is linked to the ability of the river to do work. The stream power increasingly appears as a useful parameter for regional-scale modelling because it can be estimated at the river-scale (e.g., Jain et al., 2006) and at the network scale (Orr et al., 2008). River-scale mathematical models predict a peak in the midbasin reaches both for gross and specific stream power due to the maximization of the product of the decreasing channel slope and the increasing discharge (Lawler, 1992; Knighton, 1999). Lawler (1992) postulated that this longitudinal pattern induces a midbasin peaking of the migration rate. Some published qualitative or quantitative studies corroborate this model (Lewin, 1983; Lawler et al., 1999; Schmitt et al., 2007). The bank resistance is also a critical factor determining the spatial variability of the migration rates through the resistive forces of the bank material (Hudson and Kesel, 2000; Constantine et al., 2009)
et al., 2011). Nevertheless, and the riparian vegetation (Michalkova few studies have explored the role the bank resistance at the regional-scale (Nanson and Hickin, 1986). It is therefore unclear whether modelling the migration rate at the regional-scale needs integrating a parameter characterizing the bank resistance. To address the challenge of predicting the channel migration rate at a regional scale, this study i) explored the spatial variability of the channel migration rate, ii) identified key driving variables for channel migration rates, iii) established statistical models and iv) discussed them. In order to test this approach in a wide range of contexts to catch the different critical conditions (unshifting versus shifting meanders, wandering, braiding, effects of lithological ^ne basin (French part) and the controls …), we focused on the Rho nearby coastal Mediterranean streams.

2. Regional setting ^ne River catchment area This study relates the part of the Rho situated in France (ca. 98,500 km2) and nearby coastal Mediterra^ nenean streams (ca. 18,000 km2). It corresponds to the Rho
diterranean hydrographic district of the European Water Me Framework Directive. The catchment includes several mountain ^ neranges (the Alps, Jura, Massif Central, and Vosges), while the Sao ^ ne trough forms an important geological basin. Portions of ten Rho hydro-ecoregions comprise the study area, as defined and mapped by Wasson et al. (2002). To simplify, from west to east, the region can be divided into three geological units (Fig. 1). The western boundary is formed by the Massif Central, which is an old mountain range composed of metamorphic rocks. In the center is the former ^ne and the lower Rho ^ne corridor, which trapped valley of the Sao marine, glacial, and fluvial sedimentary deposits. The Alps lay to the east, and beyond are the eastern Alps and Jura Mountains, which were formed during the late Tertiary by tectonic plate convergence and which are composed of alkaline rocks from Jurassic and Cretaceous sediments and Tertiary and Quaternary deposits. The ^ ne network has also been influenced by the recession of the Rho Mediterranean Sea during the Pliocene and different glacial periods. This west-east organization is further complicated by climatic gradients, from an oceanic climate in the northern lowlands to the increasing influence of the Mediterranean climate in the south and the montane climate in the east. The tributaries of the ^ ne have different hydrological regimes, resulting in a high Rho ^ ne seasonal and annual irregularity in flow at the mouth of the Rho ^ne River), dominated by (Olivier et al., 2009): oceanic (the Sao re, Upper Rho ^ ne), mediterranean (southern snowmelt (e.g., Ise tributaries and most rivers draining toward the Mediterranean).

3. Data collection and methodology 3.1. Sampling design We sampled a set of 99 reaches to characterize the spatial variability of the channel migration rate throughout the study area (see site location in Fig. 1C). We selected a number of 99 reaches based on a compromise between the time required for the treatment of the aerial photographs and a minimum sample size necessary for covering a large range of valley settings and fluvial patterns. The sampling strategy for locating the reaches was based on five main criteria: (i) an homogeneous coverage of the study area so as to be representative of the regional and the longitudinal variability, (ii) unconfined reaches, (iii) a minimal active channel width equal to 5 m to limit the influence of the spatial errors inherent to diachronic analysis of aerial photographs, (iv) a coverage of a large range of fluvial patterns, and (v) no evidence of human pressure from the aerial photographs (dike, upstream dam, etc.). Proximity to a hydrologic gauging station was also considered as much as possible. The study reaches are located in contrasting valley settings including upstream narrow intra-mountainous alluvial valleys (upstream Prealps, Alps, Southern Massif Central, Jura), piedmont zone valleys (right-side and left-side tributaries of ^ne), and lowland floodplains (Bressan Rift). The watershed the Rho area of the study reaches ranges from 31 to 5610 km2 with a median value equal to 253 km2 (standard deviation SD ¼ 768 km2), and the channel slope ranges from 0.14 to 29.5‰ with a median value equal to 5‰ (SD ¼ 5.9‰). We classified the sampled reaches in two groups based on their fluvial patterns. The “single-thread reaches” (68 reaches) were characterized by a unique and well-defined straight to sinuous channel. The “multi-thread reaches” (31 reaches) were characterized by a straight to sinuous active channel with more than two low-flow channels flowing through well
gay et al., 2009). The multi-thread readefined channels bars (Pie ches mainly correspond to gravel-bed-wandering and braided reaches. 3.2. GIS processing for characterizing the channel migration The active channel boundary of the study reaches was delineated based on aerial photographs from two dates, in black and white for the first survey (called here the 'initial state') and coloured for the more recent survey (called the 'contemporary state'). Historical aerial photographs were georeferenced in a geographic information system (GIS) using a first order polynomial transformation. The root mean square error varied from 0.7 to 8.0 m, with a median value equal to 2.4 m (SD ¼ 1.25 m). The active channel corresponds to the area covered by the low flow channels and the unvegetated bars (O'Connor et al., 2003). We digitized the channels for the entire aerial photographs and then readjusted the boundaries to focus on a channel reach with a homogeneous channel planform. The length of the study reaches for the initial state ranged from 871 to 9863 m with a median value equal to 4065 m. Because the bank erosion rates are highly variable in time, the temporal resolution was of primary importance for the characterization. We estimated the lateral changes at the pluri-decadal scale, which corresponded to a study period short enough to expect reaches to be in a relative dynamic equilibrium (following the time scale reference defined by Lawler, 1993), but also long enough to observe a significant channel migration in relation to the spatial errors, notably for reaches characterized by minimal shifting. The years ranged respectively from 1946 to 1993 for the initial state (with a median value equal to 1964), and from 2002 to 2006 for the contemporary state (with a median value equal to 2003). The study period varied from 10 years to 60 years with a median value equal


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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Fig. 1. (A) Location in France, (B) geological map and (C) delineation of the 14 subwatersheds based on the hydro-ecoregions (Wasson et al., 2002): I: Eastern limestone cuestas
basins; VII: Northern Massif Central basins; VIII: Southern Massif Central basins; II: Ognon basin; III: Doubs basin; IV: Southern Jura basins; V: Bressan rift basins; VI: Bas-Dauphine re basin; XII: Durance basin; XIII: Southern Alps basin; XIV: Maures and Esterel basins. Location of the study basins; IX: Western Prealps basins; X: Northern Prealps basins; XI: Ise reaches cited in the text.

to 38 years. Some study reaches were characterized by a shorter study period because of the availability of aerial photographs. The eroded and the vegetated surfaces were delineated in a GIS by overlaying the digitized active channels for both the initial and contemporary dates. The sources of spatial error are linked to optical deformation (Lawler, 1993), georeferencing (Hughes et al., 2006), and photo-interpretation, in particular due to the overhanging riparian vegetation that masks the boundaries of the active channel (Mount and Louis, 2005). The polygons with a maximal extent of less than 5 m were considered as non-significant and deleted in accordance with previous studies (Gurnell et al., 1994; Nicoll and Hickin, 2010). We also systematically reviewed the eroded and deposited polygons and deleted those which

corresponded to human impacts (channelization, embankment, bridge abutments, etc.). Migration rates were measured for both inchannel features (e.g., the bend scale for the single-thread reaches) and at the reach-scale. The erosional and depositional processes induce a lateral shift of the active channel centerline between the historic and the contemporary states. We quantified the migration rate at the scale of the geomorphic feature resulting from the intersections of the centerlines for both time periods and corresponding to the scale at which lateral changes occurred. We extracted the centerline of the active channel using the semi
gay (2011). First, automatic procedure described in Alber and Pie we measured the proportion of the eroded channel length at the reach-scale (%Ero in %) by dividing the cumulative length with


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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4

erosional processes by the total reach length. Then, we characterized the linear migration rates at the feature-scale (AbsEro-F in m.yr1) and the reach-scale (AbsEro-R in m.yr1) (Table 1). For the feature-scale measurement, we estimated the maximal eroded length orthogonally to the river bank of the initial state, and then divided the maximal eroded length by the study period. For the reach-scale measurement, we averaged the feature-scale migration rates (for which AbsEro-F > 0 m.yr1). We estimated a relative migration rate at the reach-scale (RelEro-R) by dividing AbsEro-R by the reach-averaged active channel width for the initial state (in m.yr1 per channel width) (Table 1). We measured the reach-averaged active channel width (Wac) both for the initial and contemporary states by averaging the local values estimated at transects we generated perpendicularly to the centerlines every 10 m. We measured the sinuosity of the active channel for both dates by dividing the length of the centerline by the length of the valley axis (Table 1).

3.3. Explanatory variables Additional data was provided from the database we previously
gay (2011). We delineated a set of spatial generated for Alber and Pie units at a hierarchical level corresponding to the valley segment, defined by Frissell et al. (1986) as a portion of a stream system, which is “relatively uniform in slope” and “flowing through a single bedrock type and bounded by tributary junctions or major waterfalls”. Each valley segment was generated by detecting the longitudinal discontinuities of the watershed area, using the long profile
gay, 2011). and the valley bottom width (see details in Alber and Pie For each valley segment, we calculated the channel slope, the mean watershed area and the mean valley bottom width and estimated the 1 in 2 year discharge and the gross stream power. The stream power quantifies the rate of energy expenditure in a river system, and therefore the ability of the river to do geomorphic work (Lawler, 1992). We used the following approximated expression for the gross stream power (U in Watt.m1), which corresponds to the total stream power per unit length of the channel:

U ¼ g  Sc  Q

(1)

where g is the specific weight of water (9807 N.m2), Sc is the channel slope (m.m1), and Q is a modelled 1 in 2 year discharge (m3.s1). Studies on geomorphic processes usually focus on the specific stream power, which quantifies the rate of energy expenditure per unit area of channel bed by dividing the gross stream power per the wetted width (expressed in Watt.m2). The calculation of the specific stream power requires measuring the wetted width for the

1 in 2 year discharge. Such data were not available at the regionalscale so that the modelling in the present study focused on the gross stream power. We also characterized the active channel width rated by the watershed area, which has been already iden
gay et al., 2009). tified as critical by previous authors (Pie 3.4. Statistical analysis The study area has been decomposed in subwatersheds, which are hypothesized as being homogeneous in terms of climate, geology and relief (Wasson et al., 2002). These subwatersheds defined a framework for analyzing the spatial variability of the channel migration at the regional-scale and the watershed-scale. A correlation matrix, simple and multiple regressions were used to explore the statistical links between the explanatory variables and the parameters characterizing the channel migration (%Ero, AbsEro-R, RelEro-R). We analysed the residuals of the empirical models through spatial analysis and bivariate plots. The maximal measurement error can be reasonably estimated to 0.2 m.yr1 (i.e. 10 m for a fifty-year period) so that the residuals below this threshold are considered as non significant. 4. Results 4.1. Channel planform of the study reaches The study reaches covered a large range of channel sinuosity (Sin) and active channel width (Wac). Sin ranged from 1.05 to 1.59 for the single-thread reaches with a median value (MED) equal to 1.17 and a standard deviation (SD) equal to 0.1, and from 1.01 to 1.18 (MED ¼ 1.05 and SD ¼ 0.04) for the multi-thread reaches. Wac ranged from 4.6 to 130 m for the single-thread reaches (MED ¼ 15.3 m and SD ¼ 23.4 m), and from 10.5 to 253.7 m for the multi-thread reaches (MED ¼ 82.6 m and SD ¼ 64.3 m). Wac increased with the watershed area following a power-law relationship both for the single-thread data set (Wac ¼ 1.72*A0.41, R2 ¼ 0.38) and the multi-thread data set (Wac ¼ 4.9*A0.53, R2 ¼ 0.48) (Fig. 2A). The power-law relationship was less significant for the
gay et al. total data set (Wac ¼ 2.71*A0.42, R2 ¼ 0.18). Following Pie (2009), we scaled Wac by the watershed area rated by 0.42 (Wac*), i.e. the exponent of the power-law relationship defined for the total data set for comparing the sampled reaches at the regional-scale independently on the scale effect due to the increase of the watershed area (Wac* ¼ Wac/A0.42). Wac* covered a large range of values for both the single-thread and the multi-thread reaches. The single-thread reaches were narrower (MED ¼ 9.81 m.km0.84 and SD ¼ 5.5 m.km0.84) than the multi-thread reaches (MED ¼ 1.46 m.km0.84 and SD ¼ 1.2 m.km0.84) for a given

Table 1 List of the variables used in the present study. Variables Potential controlling variables Watershed area Channel slope 1 in 2 year discharge 1 in 2 year gross stream power Channel planform Active channel width Active channel width scaled by the watershed area Sinuosity Properties of the channel migration Absolute linear migration rate Relative linear migration rate % of channel length with bank erosion

Code

Units

Feature-scale

A Sc Q2

km2

U

m3.s1 Watt.m1

Wac Wac* Sin

m m.km0.84 e

AbsEro-R RelEro-R %Ero

m.yr m.yr e

X X X X

‰

1 1

per width

Reach-scale

X

X X X

X

X X X


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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Fig. 2. (A) Relationships between the watershed area and the initial active channel width for the total data set, the single-thread reaches (crosses and white circles) and the multi-thread reaches (grey circles). Circles are proportional to AbsEro-R and the crosses correspond to reaches without any detectable bank erosion during the study period. (B) Box plots of Wac* for the subwatersheds.

watershed area, except for a few reaches. We observed significant contrasts of Wac* between subwatersheds (Fig. 2B). 4.2. Spatial variability of the channel migration rate 4.2.1. Description of the sampling Bank erosion processes occurred in 83 of the sampled reaches during the study period (%Ero > 0%), although no changes were detectable for 16 single-thread reaches (%Ero ¼ 0%) (Fig. 3A). Most of the single-thread reaches were characterized by bank erosion that occurred locally on a few adjacent or isolated single bends (MED ¼ 13.6%). Only 12 single-thread reaches were characterized by %Ero>50%. %Ero was much higher for the multi-thread reaches (MED ¼ 84.4%). Only two multi-thread reaches were characterized by %Ero < 50%. AbsEro-R ranged from 0 to 5.78 m.yr1 for the total data set (MED ¼ 0.46 m.yr1). AbsEro-R was much lower for the single-thread reaches (MED ¼ 0.3 m.yr1 and SD ¼ 0.73 m.yr1) than for the multi-thread reaches (MED ¼ 1.42 m.yr1 and SD ¼ 1.12 m.yr1). AbsEro-R and %Ero were positively linked following a power-law relationship for the total data set with %Ero > 0% (AbsEro-R ¼ 0.08*% Ero0.62, R2 ¼ 0.61) and also for the single-thread data set with % Ero > 0% (AbsEro-R ¼ 0.1*%Ero0.48, R2 ¼ 0.49) (Fig. 3B). %Ero and RelEro-R were poorly correlated (R2 ¼ 0.21 for the total data set). 4.2.2. Regional-scale variability %Ero and AbsEro-R are spatially organized at the regional-scale, i.e. there were significant contrasts between the subwatersheds (Fig. 4A). The northern part of the study area (subwatersheds I to V), the subwatersheds VII and XIV were dominated by single-thread reaches with no detectable change and low values (in comparison

5

Fig. 3. (A) Relative frequency of %Ero; (B) Relationship between %Ero and AbsEro-R for the single-thread and multi-thread data sets.

to the whole sample) of %Ero (<20.0%). In these geomorphic settings, AbsEro-R was also dominated by low values inferior to the median value for the sample. The subwatersheds II, III and IV are characterized by a set of single-thread reaches with low to intermediate values of %Ero and AbsEro-R (between 0.3 and 0.5 m.yr1), and two single-thread reaches (Ain, Doubs) with a high value of both AbsEro-R (respectively 4.25 m.yr1 and 2.74 m.yr1) and %Ero (respectively 94% and 64%). The subwatershed VI was heterogeneous both in terms of AbsEro-R and %Ero. Two single-thread reaches were characterized by high values of both %Ero (73.4e87%) and AbsEro-R (1.2e2.8 m.yr1). Inversely, two single-thread reaches were characterized by local erosional processes (%Ero < 20%) and a low value of AbsEro-R (0.28e0.38 m.yr1). The subwatershed VIII was characterized by intermediate and high AbsEro-R and %Ero, except locally. In the Alpine zone, the multi-thread reaches located in the subwatersheds IX to XIII are dominated by intermediate to high values of AbsEro-R (MED ¼ 0.48e1.44 m.yr1) and %Ero (MED ¼ 46.7e84.3%). Most of the single-thread reaches located in this geomorphic setting were characterized by bank erosion processes low in extent (%Ero < 20%) and low to intermediate AbsEro-R (0.18e0.34 m.yr1) or by no detectable change (%Ero ¼ 0%) for the study period. Only two single-thread reaches were characterized by high values of %Ero (49.2e95.5%) and AbsEro-R in subwatershed X (0.97e1.6 m.yr1). 4.2.3. Downstream pattern at the scale of the subwatershed We plotted AbsEro-R with the watershed area for examining the longitudinal pattern at the scale of the subwatershed. We observed no spatial correlation between AbsEro-R and the watershed area for the subwatersheds I, V, VI and XIV (Fig. 4B). AbsEro-R increased with the watershed area for the subwatersheds II, III and IV, IX to


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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Fig. 4. (A) Spatial variability of AbsEro-R and %Ero, (B) Downstream pattern of AbsEro-R at the scale of the subwatersheds.

XIII (AbsEro-R ¼ 0.31*A0.28, R2 ¼ 0.26 for subwatershed IX; AbsEroR ¼ 0.06*A0.52, R2 ¼ 0.16 for subwatershed X; AbsEro-R ¼ 0.05*A0.64, R2 ¼ 0.59 for subwatershed XI; AbsEro-R ¼ 0.09*A0.54, R2 ¼ 0.55 for subwatershed XII). AbsEro-R tended to peak for the intermediate values of watershed area in the Massif Central (subwatersheds VII and VIII). 4.3. Statistical analysis 4.3.1. Correlation matrix For the total data set, AbsEro-R was poorly correlated with Sc (R ¼ 0.44) and Q2 (R ¼ 0.15) (Table 2). The most significant statistical relationships between AbsEro-R and the explanatory variables were observed for U (R ¼ 0.69; p < 0.001), Wac (R ¼ 0.76; p < 0.001) and Wac* (R ¼ 0.73; p < 0.001) (Table 2). Concerning the sample of single-thread reaches, AbsEro-R was poorly correlated with Sc and Q2. The spatial correlation with AbsEro-R is positive for U (R ¼ 0.67;

p < 0.001) and Wac* (R ¼ 0.60; p < 0.001). We also observed a spatial correlation between AbsEro-R and the initial Wac (R ¼ 0.6). Concerning the multi-thread reaches, AbsEro-R correlates with Q2 (R ¼ 0.63). The correlations between AbsEro-R and U (R ¼ 0.49), Wac (R ¼ 0.54) or Wac* (R ¼ 0.20) were less significant (Table 2). U and Wac* were correlated for the total (Wac* ¼ 0.07*U0.52; R2 ¼ 0.43) and the single-thread data sets (R ¼ 0.69), but not for the multi-thread data set (R ¼ 0.28) (Table 2). 4.3.2. Empirical models Empirical models linking U and AbsEro-R (Fig. 5A) were established for the total data set (R2 ¼ 0.48; p < 0.001), the single-thread (R2 ¼ 0.45; p < 0.001) and the multi-thread data sets (R2 ¼ 0.24; p ¼ 0.0056):

Model 1A ðtotal data setÞ: AbsEro  R ¼ 0:01  U0:62

(2)


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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Fig. 4. (continued).

* U and Wac were correlated following a power-law relationship (Wac* ¼ 0.07  U0.52; R2 ¼ 0.43; pvalue < 0.01). For the values of U

Model 1B ðsingle  thread data setÞ: AbsEro  R ¼ 0:01  U0:51

(3)

Model 1C ðmulti  thread data setÞ: AbsEro  R ¼ 0:01  U0:43 (4) A bivariate empirical model based on U and Wac* was statistically significant for the total data set (R2 ¼ 0.62; p < 0.001) (Fig. 5B): *:0:51 Model 2 ðtotal data setÞ: AbsEro  R ¼ 0:01  U0:43  Wac

(5)

approximately lower than 1300 Watt.m1, Wac* was less than 2 m.km0.84, AbsEro-R and %Ero tended to increase with U for the single-thread reaches. For U > 1300 Watt.m1 and Wac* > 2 m.km84, AbsEro-R tended to increase with U for the single-thread reaches. For the multi-thread reaches, AbsEro-R tended to increase with U, but didn't correlate with Wac*, and Wac* didn't correlate with U. 4.3.3. Analysis of the residuals of the models based on the gross stream power For the single-thread reaches, we observed significant contrasts


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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Table 2 Correlation matrix between the variables characterizing the channel migration rate and the potential controlling variables for (A) the entire data set, (B) the single-thread data set and (C) the multi-thread data set. All the variables have been log-transformed. For those in bold, R is significant for p-value < 0.0001. A

Q2

Sc

Wac

Wac*

U

%Ero

AbsEro-R

RelEro-R

1.00

0.85 1.00

¡0.63 ¡0.62 1.00

0.37 0.36 0.21 1.00

0.09 0.02 0.54 0.89 1.00

0.16 0.34 0.53 0.65 0.62 1.00

%Ero AbsEro-R RelEro-R (B) A Q2 Sc Wac Wac*

0.04 0.03 0.48 0.59 0.65 0.60 1.00

0.18 0.15 0.44 0.76 0.73 0.69 0.77 1.00

0.28 0.31 0.32 0.35 0.24 0.05 0.26 0.34 1.00

1.00

0.83 1.00

¡0.74 ¡0.63 1.00

0.59 0.81 0.26 1.00

0.11 0.30 0.29 0.74 1.00

0.03 0.35 0.50 0.58 0.69 1.00

%Ero AbsEro-R RelEro-R (C) A Q2 Sc Wac Wac*

0.02 0.23 0.27 0.43 0.51 0.59 1.00

0.16 0.31 0.27 0.60 0.60 0.67 0.74 1.00

0.40 0.44 0.59 0.29 0.02 0.22 0.46 0.59 1.00

1.00

0.93 1.00

0.62 0.61 1.00

0.69 0.68 0.36 1.00

0.19 0.24 0.03 0.84 1.00

0.78 0.87 0.15 0.63 0.28 1.00

0.03 0.02 0.14 0.03 0.02 0.11 1.00

0.72 0.63 0.49 0.54 0.20 0.49 0.23 1.00

0.06 0.14 0.08 0.57 0.73 0.22 0.19 0.38 1.00

(A) A Q2 Sc Wac Wac*

U

U

U

%Ero AbsEro-R RelEro-R

of the residuals for the pattern-dependent models 1B and 1C at the regional-scale (Fig. 6A). The model 1B tended to correctly predict (residuals ranging from 0.1 to 0.1 m.yr1) or to weakly overpredict AbsEro-R for a large set of study reaches located in the northern subwatersheds I to V (positive residuals < 0.2 m.yr1), and subwatersheds IX (positive residuals < 0.34 m.yr1) and XIV (positive residuals < 0.32 m.yr1) (Fig. 6A). The residuals were locally negative in these subwatersheds for model 1B, with low values except for two reaches with high negative residuals (see below). The residuals of model 1B were heterogeneous for subwatersheds VII, VIII, and X. Model 1B loosely predicts (overpredicts or underpredicts) AbsEro-R for a set of single-thread reaches located in subwatershed VII (0.28e0.33 m.yr1), VIII (0.17e0.23 m.yr1), X (0.60 m.yr1). The model 1B strongly underpredicted AbsEro-R for a set of single-thread reaches with high values of %Ero (Fig. 6A and B) and without any clear spatial organization at the regional-scale. These were located in subwatershed III (2.16 m.yr1), IV (2.95 m.yr1), VI (0.87 and 2.61 m.yr1), VIII (0.31 to 0.75 m.yr1) and X (0.4 and 0.93 m.yr1). For the multi-thread data set, the residuals were high for the model 1C without any significant contrasts between subwatersheds VIII to XII (Fig. 6A): 0.92 m.yr1 for the subwatershed VIII, median value equal to 0.26 m.yr1 for subwatershed IX (SD ¼ 0.9 m.yr1), median value equal to 0.31 m.yr1 for subwatershed X (SD ¼ 0.6 m.yr1), median value equal to 0.14 m.yr1 for subwatershed XI (SD ¼ 0.75 m.yr1) and median value equal to 0.26 m.yr1 for subwatershed XII (SD ¼ 1.19 m.yr1). The model 1C systematically overpredicted AbsEro-R for subwatershed XIII with residuals ranging from 1.0 to 1.4 m.yr1. Most of the residuals tended to be negative for the multi-thread reaches that widened during the study period, and inversely, the residuals tended to be positive for the multi-thread reaches that narrowed (Fig. 6C). Nevertheless, there was no significant statistical

relationship between the residuals and the rate of variation of Wac*. 5. Discussion Preserving and restoring rivers at the regional-scale requires tools and geographical information for planning operations, i.e. adapting the management strategy to the ecological settings and
gay et al., 2005; Beechie et al., 2006). defining priorities (Pie Empirical models based on simple GIS-data may be an option for predicting geomorphic attributes, both morphological and functional, at the regional-scale. We open discussion issues on interest of such empirical models based on stream power, and also key controls and methodological constraints for a regional-scale modelling of the migration rate. 5.1. Regional-scale pattern of the migration rates Three variables have been used for characterizing the channel migration in the present study: the absolute (AbsEro-R) and relative (RelEro-R) migration rates currently used in published papers (e.g., Nanson and Hickin, 1986; Nicoll and Hickin, 2010; Richard et al., 2005) and the reach-scale proportion of riverbank with erosional processes (%Ero). Our data show a clear regional-scale pattern for both %Ero and AbsEro-R due to the contrasts between the subwatersheds and the upstream-to-downstream variations. Some geomorphic provinces are dominated by laterally stable rivers at the pluri-decadal scale. Inversely the channel migration occurs in some geomorphic provinces with absolute bank erosion rates that vary depending on the environmental setting. Such regional-scale pattern was also observed in Wales by Lewin (1983) and for the French Rhine basin by Schmitt et al. (2007). Assuming that the watershed area is a good proxy for the distance from the source, we didn't observe a uniform longitudinal


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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Fig. 5. Plot of the 1 in 2 year gross stream power and AbsEro-R (A) and the initial Wac* (B). The circles and the squares for (B) are proportional to AbsEro-R and correspond respectively to the single-thread and the multi-thread reaches.

pattern of AbsEro-R at the regional-scale because of the variability between subwatersheds. This result contrasts with the published empirical models that show a spatial correlation between the watershed area and the absolute migration rates at the regional (Nicoll and Hickin, 2010) or global scale (Hooke, 1980). A longitudinal trend rather emerges at the scale of the subwatershed on the study area and we observed three main patterns: (i) no trend in the subwatersheds dominated by laterally inactive rivers (e.g., Bressan rift and the massif of Maures and Esterel), (ii) increase (e.g., Alps, Jura), or (iii) midbasin peaking (southern Massif Central). Nevertheless, our data also suggest that the variability between rivers located in a given subwatershed may be high (e.g., subwatersheds IV, VI, XII). Moreover, we characterized the variability inner each subwatershed based on a very extensive sampling (low point density) so that the longitudinal trends we observed cannot be extrapolated to each river of the subwatersheds. In a summary, our observations confirm that the downstream-upstream increase model (Hooke, 1980; Hall et al., 2007) as well as the midbasin peaking model (Lawler, 1992) are scale-dependent and are valid only in specific geomorphic settings. Further work would be required to quantify more intensively the upstream to downstream

variation of the migration rates at the river-scale and identify the key factors determining the variability of the longitudinal patterns throughout the study area. Because of the large range of channel sizes among the sampled reaches, AbsEro-R would need to be scaled to compare the reaches from upstream to downstream, and also between subwatersheds (e.g., in order to identify the most laterally active rivers at the regional-scale). It is well established that the channel width scales with a characteristic discharge or with the watershed area used as a surrogate (Leopold and Maddock, 1953). Based on this background, the bankfull width measured at the inflection points is currently used as a surrogate of the dominant discharge for scaling the absolute bank erosion rates of meandering rivers (Ferguson, 1981; Nanson and Hickin, 1986). Because this method cannot be generalized to any fluvial pattern (i.e. wandering or braiding reaches), we scaled AbsEro-R by the reach-averaged active channel width measured regularly along the channel course (Wac), resulting in the relative bank erosion rate RelEro-R. The statistical analysis shows that Wac and AbsEro-R are closely linked. This result is consistent with the regional-scale studies on meandering (Nanson and Hickin,
gay et al., 1986; Nicoll and Hickin, 2010) and braiding rivers (Pie


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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Fig. 6. (A) Regional-scale mapping of the pattern-dependent gross stream power-based model for the single-thread (model 1B) and the multi-thread (model 1C) patterns; (B) variation of the residuals of model 1B with %Ero for the single-thread reaches; (C) Plot of the residuals of the model 1C (for multi-thread reaches) with the temporal variation of Wac*.

1996), and suggests that Wac would be relevant for scaling the absolute erosion rate by eliminating a size effect. Nevertheless, this study shows that no spatial pattern of RelEro-R emerged at the regional-scale and we contend that the variable RelEro-R has limitations for characterizing and modelling the rate of channel ^ne basin. migration in a highly heterogeneous basin such as the Rho
gay et al. (2009), we introduced Wac* to examine Following Pie the spatial variation of Wac superimposed to the upstreamdownstream increase due to the increasing discharge. We observed that Wac* was highly variable at the regional-scale with significant contrasts between subwatersheds. We also observed that Wac* varied significantly in time at the pluri-decadal scale, particularly for the multi-thread reaches. As a consequence, Wac may integrate the effects of additional factors so that Wac isn't a good surrogate of the dominant discharge at the regional-scale on ^ ne basin. Moreover, AbsEro-R and Wac* were spatially the Rho correlated at the regional-scale, i.e. AbsEro-R was all the more high

than Wac was high for a given watershed area. Scaling AbsEro-R by Wac would tend to minimize the relative migration rate for the reaches with a high Wac* for a given dominant discharge (or maximize for a low Wac*). The comparison of the reaches in terms of migration rate would be therefore biased at the regional-scale. In a summary, we contend that the physical meaning of the ratio between AbsEro-R and Wac is unclear and may vary at the regionalscale, i.e. RelEro-R is not a relevant indicator in a highly heteroge^ne basin. We recommend developing neous basin such as the Rho empirical models independently both for AbsEro-R and Wac, and then calculating RelEro-R only in geomorphic settings with a narrow range of Wac*. More generally, scaling migration rate is a critical methodological issue and further research is needed for developing new indicators which meet the different management objectives relative to the preservation and restoration of the channel migration (e.g., managing the global sediment budget, protecting the riparian ecosystems, etc.).


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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Fig. 6. (continued).

5.2. Key controls on the absolute channel migration rates at the regional-scale The gross stream power has been currently used for modelling the absolute migration rate (Nanson and Hickin, 1986; Richard et al., 2005; Nicoll and Hickin, 2010). The statistical analysis showed that the gross stream power was the primary explanatory variable for the regional-scale pattern of AbsEro-R for the total data set: (i) the study reaches with %Ero < 20.0% were characterized by low values of gross stream power, and (ii) the gross stream power was significantly correlated with AbsEro-R for the study reaches with %Ero > 0%. If we consider the single-thread data set, the channel slope and the 1 in 2 year discharge poorly explained AbsEro-R individually. The product of both variables significantly improved the statistical link at the regional-scale. Therefore, our data confirm results of prior studies focusing on meandering rivers (defined by a single-thread channel with a sinuosity > 1.5) in western Canada (Nanson and Hickin, 1986; Nicoll and Hickin, 2010). The gross stream power is a good explanatory variable of the regional-scale pattern of the absolute migration rate but the statistical analysis highlights the role of specific geomorphic settings. The spatial analysis of the residuals of the model 1B and C showed indeed that these residuals were regionalized, i.e. AbsEro-R was systematically overestimated or underestimated depending on the geomorphic setting. Moreover, the gross stream power based model 1B significantly underestimated AbsEro-R for a set of highly laterally active reaches. We assume than the residuals above the threshold equal to 0.2 m.yr1 cannot be explained by the

measurement errors (see methodological section). These residuals may therefore be explained by additional key variables such as the bank resistance (Hudson and Kesel, 2000; Constantine et al., 2009). Based on these statements, we may expect that the absolute migration rate would tend to systematically increase with the gross stream power at the regional-scale, with negative or positive residuals of the stream power based model depending on the boundary conditions (bank material, riparian vegetation). Nevertheless, Nanson and Hickin (1986) showed that the variance contribution of the bank-toe median grain size used as a surrogate for bank resistance was negligible for explaining the absolute migration rate at the regional-scale. Moreover, some authors suggested that bank resistance may be closely linked to the specific stream power because floodplain materials tend to be erodible in medium-to high-energy valley settings whereas they tend to be cohesive in low energy valley settings (Nanson and Croke, 1992). As a consequence, the bank resistance, alone, would explain the residuals of the stream power-based model only for rivers that don't flow in their own alluvium and for which the stream power and the sedimentary properties of the floodplain are uncorrelated. The spatial analysis of the residuals allows discussing the contribution of the bank resistance for explaining the regionalscale pattern of the absolute migration rate. On the study area, the bank resistance may explain why the models 1A and 1B tend to systematically overestimate AbsEro-R in the subwatersheds I, II, V and XIV. In these geomorphic settings, the gross stream power is insufficient for eroding the banks whatever the boundary conditions, i.e. a low bank resistance wouldn't compensate the low


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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values of gross stream power. This assumption is reinforced by the planform of these reaches that is characteristic of a cohesive sedimentary environment (regular or irregular channel course with an intermediate to high channel sinuosity, no bars, and a narrow active channel width). Except for these geomorphic settings, no interpretation can be formulated on the contribution of the bank resistance on the regional-scale pattern of the residuals. We hypothesize that the variance contribution of the bank resistance may be negligible in relation to the variance contribution of the stream power at the regional scale. A field characterization of the bank material and the riparian vegetation would be required to validate or invalidate this assumption. The model 2 also implies that the sediment supply may be a critical factor for explaining the regional-scale pattern of the absolute migration rate. Our data showed that the Wac* was spatially correlated with AbsEro-R. Because the southern Massif Central (subwatershed VIII), the Prealps and the Alps (subwatersheds IX to XIII) were characterized by a similar range of gross stream power, we hypothesize that the spatial variability of the sediment supply explains the contrasts in terms of Wac* between and within these geomorphic settings. We hypothesize that the single-thread reaches with a lower Wac* located in these geomorphic settings are supply-limited reaches. Montgomery and Buffington (1997) observed that supply-limited reaches with channel slopes ranging from 1.0 to 3.0% tended to self-organize into a plane-bed morphology. The plane-bed channels lack sufficient lateral flow convergence to develop pool-riffle morphology due to a low widthto-depth ratio and a high relative roughness, so that flows may be undiverted and the temporally-averaged near bank velocity may be insufficient for eroding the banks. We therefore hypothesize that a low sediment supply explain why a set of high-slope single-thread reaches located in the subwatersheds VIII, IX and X are characterized by significant positive residuals for model 1B. Inversely, we interpret the multi-thread reaches with a higher value of Wac* as transport-limited reaches (Fig. 7C and D). Our data showed that AbsEro-R tended to be higher for the multi-thread reaches than for the single-thread reaches for a given gross stream power. The interaction between multiple-stage flows, large gravel bars and vegetation dynamics may explain why AbsEro-R tended to be higher for the multi-thread reaches than for the single-thread reaches. Because the fluvial pattern didn't correlate with the gross stream power on the study area, its variance contribution may become critical for explaining the spatial variability of AbsEroR. Then, the model 1B significantly underestimated AbsEro-R for a set of highly-mobile single-thread reaches. These single-thread reaches were characterized by well defined chute bars and also by the highest values of Wac* for the single-thread data set (e.g., Fig. 7B) following the terminology of Kleinhans and van den Berg (2011). They tended to migrate laterally through elongation, expansion and chute cut-offs, and therefore contrasted with the less mobile meandering reaches characterized by scroll bars (e.g., Fig. 7A) (Kleinhans and van den Berg, 2011). We hypothesize that the spatial variability of the sediment supply contributes significantly to determine the regional-scale distribution of the singlethread reaches with well-defined chute bars. We also hypothesize that the flow perturbation induced by the large chute bars may explain why AbsEro-R tended to increase with Wac* for a given gross stream power for the single-thread reaches, and may therefore explain the high negative residuals of the model 1B. In a summary, we hypothesize that Wac* is a relevant surrogate the sediment supply and in-channel storage at the regional-scale ^ne basin. The spatial variation of sediment supply may for the Rho determine indirectly the regional-scale pattern of the absolute migration rate through the interaction between the flow

component and the channel morphology both for single-thread and multi-thread reaches. The longitudinal pattern of the absolute migration rate due to the variation of the gross stream power may be therefore complicated depending on the localisation of the sediment sources and the upstream-downstream connectivity. We hypothesize that the spatial variability of the sediment supply may be more critical than the bank resistance for modelling the absolute migration rate on the study area. Further research is now required for testing these hypotheses.

5.3. Feedback on methodological constraints for a regional-scale modelling 5.3.1. Quantifying the stream power at the regional-scale The statistical analysis demonstrated that the gross stream power was a good predictor of the absolute migration rate at the regional-scale. Nevertheless, the gross stream power may also have intrinsic limits for increasing the accuracy of the predictive empirical models because it quantifies too broadly the hydraulic conditions determining the migration rates in specific geomorphic settings. As discussed in the previous section, Wac* was spatially correlated with AbsEro-R at the regional-scale and may integrate indirectly the effect of sediment supply on the channel morphology and flow component. On the one hand, the gross stream power doesn't integrate the effects of the morphological properties on the hydraulic conditions and the patterning processes such as channel junction and bifurcation in multi-thread reaches or channelized flow leading to sinuosity development (Lewin and Brewer, 2001). On the other hand, the gross stream power doesn't integrate the hydraulic effects of the morphology in single-thread channels that mitigate (e.g., plane-bed) or increase (e.g., effect of chute bars) the perturbation of the near-bank velocity and therefore the migration rates (Ikeda et al., 1981). As a consequence, we hypothesize that improving the stream power based empirical model also needs to refine the quantification of the flow strength to integrate these particular hydraulic effects. Theoretically, the specific stream power should be used for modelling the bank erosion rate at the regional-scale because it quantifies the cross-sectional flow energy available. Nevertheless, the key contribution of Wac* implies a critical methodological issue for accurately estimating the specific stream power at the scale of a ^ne basin. The specific highly heterogeneous basin such as the Rho stream power is currently calculated by dividing the gross stream power by the wetted width for the characteristic discharge (e.g., the 1 in 2 year discharge). The large range of Wac* (i.e., the large scatter in the relationship between Wac and the watershed area) implies that Wac isn't a valuable surrogate of the wetted width for the 1 in 2 year discharge at the regional-scale as illustrated in Fig. 7. As a consequence, the use of Wac for scaling the gross stream power may overestimate, or inversely underestimate, the wetted width depending on the value of Wac*, and therefore the specific stream power, effectively corresponding to the 1 in 2 year discharge. We hypothesize that the specific stream power calculated from Wac may be representative of the energy available for the 1 in 2 year discharge for reaches with a low Wac* (e.g., the Allaine River, Wac* ¼ 1.2 m.km0.84 in Fig. 7A). For reaches with an intermediate to high Wac* (e.g., 4.2, 5.8 and 13.1 m.km0.84 for the Ain reach, the €ch reach, in Fig. 7B, C and D), the specific Drac reach and the Bue stream power calculated from Wac may significantly underestimate the energy really available for the 1 in 2 year discharge. Further research is needed to explore in more details these methodological and theoretical issues in order to improve the quantification of the flow strength for a regional-scale modelling.


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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Fig. 7. Diversity of the laterally active reaches in the study area: Allaine (study period: 1961e1997)(A), Ain (study period: 1963e2000)(B), Drac_2 (study period: 1981e2003)(C) and €ch (study period: 1971e2004)(D) (1 ¼ eroded area, 2 ¼ deposited area, 3 ¼ wetted width derived from a power-law function of the 1 in 2 year discharge). See location of the Bue reaches on Fig. 1.

5.3.2. Role of temporal variability and trajectory No significant statistical relationship was established between AbsEro-R and Wac* for the multi-thread reaches. Moreover, we observe no spatial pattern of the residuals of the model 1C at the regional-scale, and a correlation between these residuals and the temporal variations of Wac*. We therefore hypothesize that residuals of the model 1C are partly determined by the high sensitivity of the migration rate to the temporal scale. Indeed, the characteristic discharge used in the model is supposed to be representative of the flood series at the pluri-decadal scale. Nevertheless, the channel migration is driven by temporal variations in discharge and the temporally-averaged migration rates are much more smoothed over longer study periods (Lawler, 1993). The sensitivity of the migration rates to the temporal scale may be particularly high for the multi-thread reaches because they don't migrate laterally in a progressive and continuous manner over space and time (Ferguson, 1993). Hickin and Sichingabula (1988) have described the planform changes of the braided Squamish River in response to a rare event.

They observed that the study reach was tending to a single-thread channel pattern following a progressive 40-years narrowing trend before the flood, with a large part of the braided zone corresponding to abandoned back channels, inactive braid bars, and islands. The consequence of the large flood was to “fully reinstate the braided character of the channel” through a widening and a straightening of the active channel. Hickin and Sichingabula (1988) interpreted these drastic changes by the fact that the flood was “the only event in the last 40 years to flow over the surface of the major bars and islands”. Hypothesizing that such behaviour is characteristic for braided rivers, their migration rates may be highly sensitive both to (i) the flood series before the survey that determines the initial state, and to (ii) the occurrence of a large flood (e.g., 1-in-10 year discharge for South-east French multi-thread rivers) during the survey period (Belletti et al., 2013). The temporal snapshot used in this study may be therefore a factor that explains a large part of residuals of the gross stream power based model for the multithread reaches. This aspect needs to be considered in more detail by future research.


gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055

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5.3.3. Role of the anthropogenic features We assume that the contribution of human controls for explaining the residuals of the empirical models is probably limited in the present study because of the criteria used for the sampling which focused on alluvial reaches without any evidence of human impact from aerial photographs. Nevertheless, they may contribute to explain locally the residuals for a set of sampled reaches. SouthEast French rivers have been indeed extensively altered since the eighteenth century for producing hydroelectricity, extracting gravel or reducing flood risks and bank erosion (Bravard et al., 1999;
bault and Pie
gay, 2002). Bank armoring and dikes could not be Lie systematically detected from aerial photographs, especially for the older anthropogenic features. As a consequence, some study reaches may have been locally artificially constrained so that both AbsEro-R and %Ero may be partly explained by anthropogenic features. Moreover, human impacts may indirectly influence the migration rates due to upstream or downstream constraints on flow and sediment discharge such as dams (Shields et al., 2000; Richard et al., 2005). In particular, our data show significant unbalanced erosional and depositional processes for the multi-thread reaches that may be explained by the temporal variation of the discharge, but that may be also interpreted as channel adjustment due to human impacts. Characterizing the anthropogenic features which control the migration rate is a critical issue for extrapolating the empirical model to the whole stream network. 6. Conclusion The present study aimed to characterize and model the spatial variability of channel migration rates at the regional-scale in a highly heterogeneous stream network. Our data demonstrated that the absolute migration rates were spatially organized at the regional-scale so that developing empirical models is relevant for helping managers to adapt the management strategy. For example, the empirical model may be useful for targeting or prioritizing laterally active rivers or reaches for which the erosional processes should be preserved or restored through the delineation of an erodible corridor and specific recommendations for managing bank protection and landscape planning with regards to human infra
gay et al., 2005). The gross stream power appeared to structure (Pie be the best predictor for modelling the absolute migration rate at the regional-scale and identifying the geomorphic settings dominated by laterally active rivers. Nevertheless, some limits of the gross stream power emerged from the present study, as well as the potential contribution of additional factors (e.g., bank resistance and sediment supply). Several methodological issues have been discussed for improving our capabilities to model morphological and functional attributes at the regional-scale. Acknowledgements The authors kindly thank the Water Agency that is supporting the research and the ISIG technical platform of the ENS-Lyon. The research is conducted in the LTER group of the ZABR (Zone Atelier ^ne) and the IWRnet FORCASTER project (2008e2010). Bassin du Rho Adrien Alber received a Ph.D. grant from the French Ministry of Agriculture. Robin Jenkinson is also thanked for proofreading the manuscript. References
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gay, H., Characterizing and modelling river channel migration rates at a regional scale: Case study Please cite this article in press as: Alber, A., Pie of south-east France, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.10.055