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TRANSCRIPT
Assessing and mitigating large wood-related hazards in mountain
streams: recent approaches
B. Mazzorana a,b*, V. Ruiz-Villanueva c,d, L. Marchi e, M. Cavalli e, B. Gems f, T. Gschnitzer g, L. Mao h, A. Iroumé b,i, G. Valdebenito b,j
aUniversidad Austral de Chile, Faculty of Science, Instituto de Ciencias de la Tierra,
Valdivia, Chile bUniversidad Austral de Chile, RINA – Research Nucleus on Natural and
Anthropogenic Risk, Valdivia, Chile cUniversity of Bern, Dendrolab.ch, Institute of Geological Sciences, Bern, Switzerland dUniversity of Geneva, Institute for Environmental Sciences, Geneva, Switzerland.e CNR – IRPI, Padova, Italyf University of Innsbruck, Unit of Hydraulic Engineering, Department for Infrastructure,
Innsbruck, AustriagGeotechnik Team, Engineering Office, Innsbruck, AustriahPontificia Universidad Católica de Chile, Department of Ecosystems and
Environments, Santiago, ChileiUniversidad Austral de Chile, Faculty of Forest Sciences and Natural Resources,
Valdivia, Chile jUniversidad Austral de Chile, Faculty of Engineering, Valdivia, Chile
*Corresponding author: Bruno Mazzorana, [email protected]
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Abstract
The assessment and mitigation of floods in mountain streams, when large wood
(LW) is transported, pose several challenges. The process chain consisting of flood
propagation, large wood recruitment, entrainment, transport and entrapment triggers, at
critical sections such as bridges, unexpected and exacerbated impacts to the exposed
built environment. We provide a review on the recent advances in modelling LW
dynamics during extreme river floods through computational approaches. Moreover, we
discuss how scaled flume experiments can enhance process understanding at critical
flow sections such as bridges to address risk mitigation problems. We also present a
framework based on Formative Scenario Analysis (FSA) to derive consistent hazard
process scenarios in steep mountain streams segments. As to consolidate the state of the
art, the presented set of assessment methods allow for a reliable application of the
integral risk management model conceptualized as a risk cycle also for LW related
hazard processes since the effectiveness of mitigation critically depends on the acquired
processes understanding.
Keywords: floods, formative scenario analysis, flume experiments, large wood,
numerical modelling, risk management
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1. Introduction
Mountain areas are characterized by complex geological and geomorphological
settings and often display high hillslope-channel connectivity (Harvey 2001; Cavalli et
al. 2013; Bracken et al. 2015), resulting in very high flood hazard due to a rapid
hydrological response associated to instability phenomena on hillslopes, intense
sediment transport and important morphological changes in the channel network (Borga
et al. 2014; Comiti et al. 2016a; Surian et al. 2016). In forested mountain areas, the
transport of large quantities of wood material, hereafter referred to as large wood (LW),
can be an exacerbating hazard factor (Diehl 1997; Comiti et al. 2006; Mazzorana et al.
2011b; Schmocker and Hager 2011; Mao et al. 2013; Ruiz-Villanueva et al. 2014a;
Gschnitzer et al. 2015; Gschnitzer et al. 2016). Moreover, the urbanization, the
development of infrastructures, the occupation (e.g. intense land use such as recreation
areas, ski resorts, hiking paths, etc.) and the increasing economic value in endangered
zones of mountain areas make these regions particularly exposed to the impacts of flood
events (Comiti et al. 2006; Tacnet et al. 2012; Versini et al. 2010; Mazzorana et al.
2011a) and special procedures in flood risk management are demanded (Spreafico
2006).
The physical and biological benefits of LW in rivers are currently well recognized
(Swanson et al. 1976; Gurnell 2013; Le Lay et al. 2013; Wohl 2013; 2017; Ruiz-
Villanueva et al. 2016a). Large wood influences the diversity, abundance, biomass, and
production of stream invertebrates (Benke and Wallace 2003; Harmon et al. 1986).
Great attention in the literature was deserved to in-channel LW storage and its physical
and morphological effects (see for a summary Gurnell et al. 2002; Gurnell 2013; Le Lay
et al. 2013; Ruiz-Villanueva et al. 2016a; Wohl 2017). However, our current knowledge
of wood transport during floods is still very scarce (Comiti et al. 2016a). In this context,
it is worth highlighting the added value of documenting extreme flood events deserving
particular attention to assess recruited, transported and deposited LW volumes and
inferring the event-related LW dynamics (Badoux et al. 2014, 2015; Lucía et al. 2015;
Rickenmann et al. 2015; Comiti et al. 2016a; Steeb et al. 2017). The data collected after
flood events are useful to develop relationships between the exported wood volume and
the maximum discharge of the flood, or other parameters (Rickenmann 1997; Steeb et
al. 2017), although a reliable prediction of potential wood volumes and transport rates
remains to be developed. In addition, post-event data are helpful to calibrate existing
LW transport models (Mazzorana et al. 2009b; 2011; Rigon et al. 2012; Ruiz-
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Villanueva et al. 2013; 2014a; 2014b). Moreover, such data are crucial for approaching
properly flood hazard mitigation plans in forested river basins. Enhancing the quality of
hazard prediction and mitigation design is an essential requirement to increase
preparedness when the exposed system (i.e., urbanized areas, infrastructures, recreation
areas, etc.) is affected by a flood event and to generate knowledge to re-design less
vulnerable system configurations (compare Anderson 2000; Wisner et al. 2004; Fuchs
2009a).
However, long-term observations of LW dynamics are still extremely rare,
although methods for monitoring and tracking LW are progressing rapidly (MacVicar
and Piégay 2012; Ravazzolo et al. 2015). Several field investigations have been
conducted in mountain rivers (e.g., Faustini and Jones 2003; Gurnell 2003; Andreoli et
al. 2007; Mao et al. 2008; Wohl and Goode 2008; Iroumé et al. 2015), and modelling
approaches are also increasingly being used to explore LW dynamics (Welber et al.
2013; Bertoldi et al. 2014; Ruiz-Villanueva et al. 2014a).
A LW-laden flood can be considered as a process chain of mutual interactions
between flood propagation, sediment transport, LW recruitment, LW entrainment, LW
transport and LW deposition (Mazzorana and Fuchs 2010a). In the recent past, river
managers have recognized the role of LW transport as potential risk amplifier
(Mazzorana and Fuchs 2010a). As a consequence, LW is being included in some hazard
and risk assessment guidelines (Mazzorana et al. 2011a; Mao et al. 2013; Wohl et al.
2016).
Yet the most common measure to avoid wood-related hazards is the systematic
wood removal from rivers (Wohl 2014), usually without evaluating ecological and
morphological benefits of wood (Wohl et al. 2016). Wood removal might however
result in irreversible changes in the fluvial ecosystem (Brooks et al. 2003; Collins et al.
2012) and may be unsuccessful because of new inputs during sequenced flood events
(Young 1991; Gippel 1995; Dudley et al. 1998). Moreover, in economic terms, it might
be more expensive than modifying an infrastructure to allow LW passage (Lassettre and
Kondolf 2012).
Recently different authors reviewed several aspects of LW dynamics in rivers:
Comiti et al. (2016) presented an overview of LW recruitment and transport during
floods in mountain areas, with some recent examples from Switzerland and Italy.
Kramer and Wohl, 2016 and Wohl, 2017 summarized current knowledge regarding LW
transport, and Ruiz-Villanueva et al. 2016 provided a review of recent advances in the
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field of LW dynamics. In some of these works, the role of LW as a potential hazard is
mentioned, but any of them is entirely focused on this aspect. A partial exception is the
work presented by Wohl et al. (2016). Therefore, in this work, we focus for the first
time on assessing and mitigating LW-related hazards, bringing together different tools
and approaches, such as novel computational and experimental (i.e. physical) models
recently presented. We first outline recent advances in LW-transport numerical
modelling in rivers (section 2). Then, we describe how experimental modelling can
provide essential knowledge elements on process behavior at critical flow sections such
as bridges (section 3). We also present a framework based on Formative Scenario
Analysis (FSA) to derive consistent hazard process scenarios based on expert
knowledge (section 4). In section 5 we discuss how this set of assessment methods can
promote the effectiveness of preventive management actions (Carter 1991; Alexander
2000; Kienholz et al. 2004; Fuchs 2009a; 2009b). We focus on mountain areas,
however, it is worth highlighting that the approaches described here can be also adapted
to be applied in other environments such as lowland rivers. In the last section we
summaries the main conclusions of the work and provide some concluding remarks.
2 Numerical modelling of LW dynamics and related hazards
Most research on wood dynamics computational modelling uses simplified
continuity or Manning equations to estimate flow conditions and to further analyze
wood motion and deposition (Braudrick et al. 2001; Wilcox and Wohl 2006; Bocchiola
2011). 1D or 2D hydraulic models or a combination of both (Merten et al. 2010; Comiti
et al. 2012; Hafs et al. 2014) can be also used in a sequence, first, getting the hydraulic
model results and then computing wood mobilization and deposition. Mazzorana et al.
(2011), presented a model to delineate the possible pathways for a given wood volume
using results from a 2D hydrodynamic simulation for different time steps based on a
cell-by-cell analysis and on unsteady conditions. In their work, a simplified assessment
procedure for entrapment and deposition phenomena at obstacles (e.g., bridges, weirs,
etc.) was used (Mazzorana et al. 2011b). This approach was recently modified and
applied to two mountain streams in Switzerland (Galatioto 2016).
In a further step, the numerical model developed by Ruiz-Villanueva et al.
(2014a), Iber-Wood, simulates the transport of individual wood elements (assuming
logs as cylinders) of different sizes at short timescales fully coupled to the
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hydrodynamics (solved by the 2D De Saint Venant equations using a finite volume
method). The incipient motion of logs is determined in the model by the balance of
forces acting on the center mass of the log following the approach of Braudrick and
Grant (2000), but adjusted to the two-dimensions. Details about the model are provided
by Ruiz-Villanueva et al. (2014a; 2014c) and Bladé et al. (2016).
Numerical models, such as Iber-Wood, can be used for back analysis (i.e., to
reconstruct a past flood event), to test hypotheses (such as the analysis of factors
controlling LW transport for different river morphologies) or to analyze possible
scenarios (e.g. future floods for different return periods, floods caused by land-use
changes, etc.). A straightforward approach should be based on a multi-run scenario
simulation in order to include the complexity and stochastic variability of LW dynamics
in such as deterministic model. Therefore, in-depth knowledge of the river dynamics,
field observations and uncertainty estimations are essential.
As an example of a back analysis, the Iber-Wood model was applied to the
Cabrera Stream in Spain (Figure 1). In this small mountain river, a flash flood occurred
in December 1997, which triggered significant geomorphic changes in the stream and
recruited large amount of trees, resulting into clogging of several river sections. The
accumulation patterns of LW along the reach were computed by the numerical model
and compared with post-event field photographs, showing that the model succeeded in
reconstructing the LW deposition patterns and identifying areas of preferential log jam
formation (Ruiz-Villanueva et al. 2014b, Figure 1). The follow-up study carried out in
the Arenal River (Spain) demonstrated that the clogging of bridges could increase the
flood risk (in terms of expected damage costs) up to 50% (Ruiz-Villanueva et al.
2014d). In addition, the multi-scenario numerical modelling results allowed identifying
the most critical infrastructure along the river in the town of Arenas de San Pedro
(Spain).
Figure 1: Simulation result for the 1997 flood in Cabrera Stream (Spain):
scenario without (A and C) wood and with wood transport (B and D). Brown lines
represent logs. Color scale shows water depth (in meters) and flow velocity (in meters
per second). Numbers refer to water depth at specific cross sections. Flow direction
from bottom to top.
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The Iber-Wood model was also applied to explore the factors (such as wood piece
size, flow conditions and river morphology) controlling wood transport and retention of
LW in different reaches of the Czarny Dunajec River in Poland (Ruiz-Villanueva et al.
2016b; 2016c; 2016d; 2016e). Modelling results showed that the transport capacity in
single thread reaches is higher than in multi-thread reaches (Figure 2A). A nonlinear
relationship between transport rate and flood magnitude was observed with a tipping
point close to the bankfull discharge (compare threshold 1 in Figure 2B). The
preferential deposition sites for LW were identified computing a depositional
probability from the ensemble of the multi-scenario runs (Ruiz-Villanueva et al. 2016c).
Simulating unsteady flow conditions, Ruiz-Villanueva et al. (2016e) analyzed the
influence of a flood hydrograph (in terms of peak discharge, time to peak and total flood
duration) on the transport of previously deposited wood and new input of wood from
upstream. Results revealed a lag between the beginning of a flood and large wood
remobilization, which is related to the antecedent flood responsible for the initial wood
deposition (see details in Ruiz-Villanueva et al. 2016e). Furthermore, during a flood the
peak in modelled LW transport was generally reached before the flood peak (Figure
2C). During the falling flood limb, LW transport is likely negligible, describing a
clockwise hysteresis between discharge and LW transport.
Figure 2: Conceptual models based on numerical modelling and field
observations in the Czarny Dunajec River (Poland): (A) Relationship between log
volume and wood transport; (B) Relationship between discharge and wood transport;
(C) Relationship between the flood hydrograph and wood flux.
In a different reach of the same river, where the river crosses the town of
Długopole, the potential clogging of the main bridge was also evaluated using Iber-
Wood (Ruiz-Villanueva et al. 2017). In this reach, river morphology upstream of the
bridge was found to have double significance for bridge clogging. First, more sinuous
morphology induces the modification of the flow velocity field and water depth (i.e.,
lower velocity), and decreases clogging, as logs are more likely deposited upstream the
bridge. Second, the multi-thread river morphology with a high capacity for wood
retention, also reduces the amount of logs that can potentially clog the bridge.
Therefore, the preservation of such river reaches upstream of vulnerable sites might be
essential for the efficient reduction of LW-related flood hazard (Mikuś et al. 2016).
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The 2D modelling approach appears to be a powerful tool to analyze LW dynamics at
the reach scale, to asses LW-related hazards and to design mitigation measures;
however, some limitations need to be considered, such as the geometry of logs as
cylinders (see Ruiz-Villanueva et al. 2014a; 2017) or the implications assumed by the
two-dimensional approximation (i.e., depth-averaged hydrodynamics equations).
Further developments should try to overcome some of these constrains (e.g., Bladé et al.
2016; Allen and Smith 2012).
3 Physical modelling LW dynamics and related hazards
Physical scale model tests are generally applied in hydraulic engineering (e.g.,
Armanini et al. 2010; Schmocker and Hager 2013; Schmocker and Weitbrecht 2013;
Gems et al. 2014a; 2014b; Schmocker et al. 2015). The obtained insights into process
dynamics are particularly valuable (i) for the development, calibration and validation of
computational models and (ii) for analyses of complex flow processes and its mutual
influences, which cannot be accurately simulated by numerical models or for which no
physical-mathematical formulation has been proposed so far (Bocchiola et al. 2006;
Gems et al. 2014a; 2014b; De Cicco et al. 2015; Gems et al. 2016; Kammerlander et al.
2016). However, limitations are also present in laboratory experiments, mainly related
to scale issues (e.g., Aufleger 2006; Webb et al. 2010; Heller 2011). Physical models
for open channel hydraulics are usually scaled according to Froude similarity (see
Figure 3). Whereas geometry and hydraulics are usually scaled to laboratory dimensions
without any relevant scale effects, only the principal characteristics of LW can be
accurately considered. By representing the most relevant dimension parameters (length
and diameters of the logs) either smooth logs, logs with a structured set of branches or
root stocks have been used for different laboratory analyses (Lange and Bezzola 2006;
Sendlhofer 2010; Schmocker and Hager 2011; Gems et al. 2012; Hartlieb 2012;
Gschnitzer et al. 2013; 2014a; 2014b; De Cicco et al. 2015). The effect of fine material
(i.e., fine sediments, leaves, etc.) has been rarely considered (Schalko et al. 2016). The
same holds for the influence of the elastic modulus of the LW since this material
parameter is particularly variable in prototype conditions and, insofar woody material is
used also in lab, associated scale effects are unavoidable (Hartlieb 2012; Gschnitzer et
al. 2014c).
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Pioneering flume experiments on LW-related issues aimed at investigating wood
transport regime, incipient log motion and the transport and formation of wood
accumulations in front of obstacles such as bridge piers or decks (e.g., Lagasse et al.
2010; Schmocker and Hager 2011; Gschnitzer et al. 2013). More recently, also the
interactions between wood and morphodynamics (Bertoldi et al. 2014) and wood,
vegetation and morphodynamics (Bertoldi et al. 2015) were analyzed in laboratory
conditions. Figure 3 illustrates a sketch of the laboratory flume at the University of
Innsbruck, which has been extensively used for experiments of LW clogging at bridges
(Gschnitzer et al. 2013; 2014a; 2014b).
To reliably determine LW clogging probabilities and the associated hydraulic
effects a large set of scenarios has to be analyzed together with a number of repetitions
(Gschnitzer et al. in press). In order to approximate typical prototype conditions,
Gschnitzer et al. (2013; 2014a; 2014b) considered in their flume experiments
overflowable embankments at the lower bridge deck level.
Figure 3: Front and plan view of the laboratory flume at the University of
Innsbruck; points 1-15 denote to ultrasonic measurements of water level; relevant scale
factors according Froude similarity for scale models of open channel flow (and
experiments with LW clogging at bridges) (adopted from Schöberl (1979) and
Gschnitzer et al. (2014c)).
According to the results obtained in these experiments, flow conditions
significantly influence clogging probabilities, the formation of LW at the upstream edge
of the bridge and corresponding effects on the hydraulics (see Figure 4). Clogging
probability increased with increasing initial water level, increasing channel gradient if
the bridge was initially dammed and with decreasing channel gradient if the bridge was
initially not dammed. This influence of the freeboard at the bridge compared to the
critical dimensions of LW and the flow conditions in terms of Froude numbers on the
clogging probabilities was also observed by Schmocker and Hager (2011). Concerning
the characteristics of the LW, clogging probability increased with increasing log length,
increasing number of branches per log and an increasing trend to a congested transport
(as defined in Braudrick et al. 1997) of LW upstream the bridge. Focusing on the
structure of the bridge, smooth surface structures favored an unscathed transfer of LW
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at the bridge, whereas the presence of railings significantly favored clogging at
situations with a relatively small freeboard. Schmocker and Hager (2011) analyzed the
clogging behavior of natural logs without branches and rootstocks at different types of
bridges, such as a bridge with a single bridge deck, a truss bridge, a railing bridge and a
baffle bridge. Flow conditions and the channel gradient also influence the characteristics
of the LW accumulation at the bridge: a comparatively compact structure of
accumulated LW with a rather short extent towards upstream is expected at supercritical
conditions and rather high channel gradients. At flow conditions with small velocities,
as also typical in the vicinity of reservoir spillways (Hartlieb 2012), LW accumulates
loosely and mainly on the water surface. For the latter, the closing degree of the channel
cross section due to accumulated LW is comparatively smaller and the influence on
channel hydraulics in terms of damming up and overbank flooding is less significant
compared to conditions with high flow velocities. The presence of fines (i.e. fine
sediments, leaves, etc.) within the accumulated LW further increases the closing degree
of the bridge cross section significantly (Gschnitzer et al. 2014c; Schalko et al. 2016).
Figure 4: Damming caused by a congested input of LW (10 pieces each with
branches); initial flow conditions with water levels at lower bridge deck level (at the
upstream edge of the bridge) and channel gradients I of 0.07 % (left), 0.38 % (middle)
and 0.80 % (right); flow direction from left to right (Gschnitzer et al. 2013; 2014c).
4. Watershed scale analysis of LW dynamics: wood recruitment and delivery
The two approaches described in sections 2 and 3 were mainly applied at the river
reach scale, however, a suitable catchment-wide approach (i.e., integrated basin
management strategy) might be required to analyze processes such as those recruiting
and delivering LW to the watercourses, when setting up boundary conditions for the
numerical or physical modelling or designing mitigation measures.
Models, mainly conceptual, have been used largely to estimate wood recruitment
to streams. The earliest models were designed to simulate the delivery of wood to
streams from adjacent riparian forest (Van Sickle and Gregory, 1990), using the stand
dynamics to estimate the probability of fall (normally as a function of tree height,
distance from the channel and fall direction, some including also breakage and decay;
Bragg, 2000). They were generally based on empirical information and the relationship
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with some basin parameters (Rickenmann 1997) and on a deterministic basis, with only
few developed with a stochastic approach (see the revision made by Gregory et al.,
2003). These first attempts provided the basis for forthcoming models. Incorporating
different geomorphic processes for estimating wood delivery, Martin and Benda (2001)
and Benda and Sias (2003) developed the first model to quantify wood budget and
evaluate the spatial control on wood recruitment and distribution at multi-annual and
annual timescales, which is still extensively used nowadays (Benda and Bigelow, 2014).
After them, some more detailed models have been used at the regional and catchment
scales to identify the potential source areas of wood and to compute potential recruitable
volumes of wood and transport rates (Marcus et al., 2011; Wohl, 2011). Recent
approaches used geographic information systems (GIS; Mazzorana et al., 2009; Rigon
et al. 2012; Lucia et al., 2015b), or combined GIS with fuzzy-logic principles (Ruiz-
Villanueva et al. 2014a). However, a reliable prediction of wood volumes and transport rates is still an open question. Even when we are able to quantify wood budget, the knowledge about entrainment and transport processes is still limited since there is a lack of accurate field data
with which to interpret wood volumes and spatial distributions, parameterize equations
on wood recruitment, and test hypotheses.
5 Formative scenario analysis for LW-related hazards assessment
In steep mountain streams and torrents, the complex interactions between
hydrological forcing, sediment and LW availability, topographic settings, shallow
landslides on the hillslopes connected to the channel, and flood control structures cause
LW dynamics to be subject to multiple processes, which may be difficult to reproduce
numerically or in a laboratory. Therefore, under certain circumstances, the analysis of
LW-related hazards may require other tools more suitable for the implementation of
consistent event scenarios. Mazzorana et al. (2009a) defined a consistency matrix, based
on expert knowledge integrated through Formative Scenario Analysis (Scholz and
Tietje 2002). This method allows the identification of plausible hazard process
scenarios. The method proposed by Mazzorana et al. (2009a), which was further
extended by Mazzorana and Fuchs (2010a) to LW transport and related risks, represents
a balanced framework based on the integration of available and retrievable qualitative
and quantitative knowledge of uncertainties. First, the analyzed stream is divided into
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segments, and then the following steps were proposed (compare Mazzorana et al. 2012
for details): a) identification and selection of the relevant impact variables; b) definition
of the impact levels for each individual impact variable; c) construction of a
consistency matrix which contains the ratings for all pairs of impact variables at all
levels; d) identification of the most consistent process scenarios based on well-
established consistency measures (e.g. multiplicative consistency, MCM, as proposed
by Tietje (2005)). The core of the procedure is the construction of the consistency
matrix as the interactions between the factors controlling the system response and the
definition of hazard scenario trajectories that are encoded in them. The matrix proposed
by Mazzorana et al. (2012) for the analysis of system dynamics in steep mountain
streams is here adapted for the analysis of LW-related hazards. The adapted matrix for
LW transport scenarios consists of thirteen impact variables grouped into four groups. A
table with the list of impact variables and levels is available as additional material. The
first two groups, labelled US and DS, represent the boundary conditions at the upstream
and downstream ends of a channel segment, respectively. Both US and DS include two
impact variables each, related (US1 and DS1) to flow processes (i.e. water flood, debris
flood, debris flow) and (US2 and DS2) amount of LW transport (high: wood volume
blocking the channel, moderate: relevant amounts of wood, but no channel congestion,
and negligible to low: only few logs transported). The third group (IC, initial channel
conditions) includes six impact variables portraying recruitment, transport and storage
of LW and sediment: IC1 natural stability of the channel bed; IC2 approximate volume
of natural (floodplains) or artificial (reservoirs and retention basins upstream of check
dams) areas acting as sediment and LW traps during floods; IC3 presence of protection
structures (sills and check dams); IC4 amount of LW in the channel and potential
recruitment in the floodplain; IC5 potential recruitment of wood from hillslopes (by
wind-throw, snow avalanches, landslides, bank failure) and small steep tributaries
(mostly by debris flows); IC6 mobility of LW, estimated based on the relations between
channel width and length of logs or on the outcomes of physical or numerical
modelling. The fourth group (AD, adjustment descriptors) includes three variables that
depict possible variations in channel conditions that could affect the transport of
sediment and LW: AD1 changes (if any) in the type of flow process in the studied
channel (e.g., transition from debris flow to fluvial bedload transport); AD2 possible
failure of grade control dams; AD3 channel response to lateral inputs of sediment and
LW (LW and sediment causing channel obstruction with subsequent dam break flows;
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LW and sediment input but channel obstructions are not expected; low LW and
sediment input).
For every variable, two or more levels are possible: they contribute to identify the
conditions for the channel reach under investigation (e.g. the adjustment descriptor
variable “AD3” features three associated variable levels: “Temporary channel
obstruction with subsequent dam-break flow is expected to occur” – “AD3,1”; “Large
LW and sediment input, but channel obstructions are not expected” – “AD3,2”; “Low
LW and sediment input” –“AD3,3”).
The matrix contains ratings for all pairs of variables at all levels. The relationships
can be tagged as “neutral” when they identify combinations possible but not especially
meaningful in the context of LW transport, “inconsistent” ( e.g., high mobility of LW is
inconsistent with negligible LW export from the channel reach considered), or “fully
consistent” ( e.g., large LW input from unstable hillslopes is fully consistent with
channel clogging and possible outburst flood caused by the failure of the temporary LW
dam). A negative value (-1) is used as a tag for excluding inconsistent combinations
from event trajectories; a value = 1 pertains to “neutral” combinations, and a value = 3
to highly consistent relationships. Other values could be chosen; higher values are
recommended for the most consistent combinations. Different metrics (e.g. summation
or multiplication of the values in matrix cells) can be used for assessing event scenarios.
A multiplicative consistency measure (MCM), computed as the multiplication of the
ratings of all consistent and neutral combinations, emphasizes highly consistent
combinations (value = 3). Event scenarios get a multiplicative consistency measure
MCM=3n, where n is the number of such relationships. To illustrate the application of
this approach, we applied it to a hypothetical segment of a steep mountain channel with
unstable forested slopes in which transition from debris flood (DFD) to debris flow
(DFW) might be associated to the increase of LW supply and transport. Given upstream
boundary conditions (US) and initial conditions (IC), a reduced matrix (Figure 5) was
set up to develop various scenarios trajectories taking into account different
combinations of adjustment factors AD.
Six scenario trajectories are listed in decreasing order of MCM (Table 1). The
matrix identifies inconsistent combinations between levels of impact variables, and
makes it possible to quantitatively compare different scenarios. This approach permits
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to valorize qualitative information. Therefore, the use of consistency matrices is
especially useful in data-poor regions, in which quantitative data on recruitment,
transport and deposition of LW are scanty or absent. The application of the consistency
matrix for LW, however, can be envisaged also in regions where data on flood
processes and LW transport are more abundant, for defining event scenarios in the cases
when more data-demanding modelling approaches are deemed not necessary.
Figure 5. Reduced consistency matrix for the assessment of evolution scenarios
corresponding to the transition from debris flood to debris flows associated to an
increase of LW transport.
Table 1. Consistent evolution scenarios ranked in decreasing order of
multiplicative consistency measures (MCM).
The design of the matrix and definition of all variables should be based on an in-
depth knowledge of the stream to be evaluated, which can be complemented by the
application of different methods. Among these, we remind the methodologies developed
for characterizing wood recruitment described in Section 4 and models as those
described in sections 2 and 3. A possible problem in the use of consistency matrices is
that they rely on expert judgement on the interactions between complex processes
related to water and sediment flows and LW recruitment and transport. The subjectivity
of such evaluations can make it difficult to identify univocally event trajectories.
Moreover, the system complexity gives rise to a twofold risk: too few variables and
impact levels in the matrix are insufficient for representing the system, whereas too
many would cause the matrix to become an unpractical and unmanageable tool. In order
to cope with these problems, the update of consistency matrices with new information is
recommended. It is also possible to envisage simplified versions for use in areas where
the whole set of controlling factors considered in this section is not operating (as an
example, the impact of torrent control works on LW transport is fundamental in many
streams of the European Alps, but it could be disregarded in other geographical regions
where these artificial structures are less common).
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6 LW-related flood risk mitigation
According to the dynamic notion of flood risk (Mazzorana et al. 2013; Fuchs and
Glade 2016) and to the model of integral risk management conceptualized as “risk
cycle” (compare www.nahris.ch; Carter 1991; Alexander 2000; Kienholz et al. 2004;
Fuchs 2009b; Bubeck et al. 2016), a temporal integration scheme can be adopted to
approach LW-related flood risk. The adopted mitigation alternatives may affect the
future trajectory of flood risk, in a way that its structure can be represented through a
circular trajectory (see Figure 6).
Figure 6. Continuum of the LW-related flood risk mitigation trajectory – circular
representation with identification of the planning-management milestones PMMs.
According to Figure 6, LW-related flood risk is evolving as a continuum over
time. Once a hazard process is triggered ( ) a series of elements concur to determine
the impacts for a given flood exposed system.
At the vulnerability of the system is largely determined by the previous
historical trajectory of the society’s capacity (i) to recover from past events, (ii) to
adjust the configuration of the elements of the built environment, (iii) to adapt its
organizational structure (both institutional and social), and (iv) to put efforts in
conceiving and implementing active and passive risk mitigation strategies (compare
Mazzorana and Fuchs 2010b; Ballesteros-Cánovas et al. 2016). To a variable extent,
civil protection strategies on the one hand and self-preparedness capabilities on the
other hand may concur to reduce the physical vulnerability (e.g., early warning,
evacuation operations, damage reduction through temporary protection measures).
In this conceptual scheme the existence of capacities to displace exposed elements
and capacities to resist the impacts of the process are recognized. In the proposed
scheme, by convention, the duration of the physical process impact and the associated
direct system responses is . The capacity to cope with a new setting is tightly
associated to the level of social learning and the existing risk culture. It may even start
at: . Three elements are fundamental for the determination of the overall
effectiveness of the deployed capacities: knowledge, degrees of freedom in design and
15
implementation, and available resources (Nowacki and Backnik 2016). The degrees of
freedom in the design domain are also determined by the level of the risk culture in the
concerned society (Renn 2008).
As outlined in the previous sections, gaining qualified and quantified knowledge
about the possible process-impact-response chains, namely about what might happen
between and what might be the underlying root causation mechanisms (at
times ), is the fundamental starting point of flood impact assessment.
Along this time trajectory we identify five representative temporal hotspots or
planning-management milestones (PMMs), where actions could be taken to mitigate the
adverse effects of LW-related flood events.
For instance, in Figure 6 the position of the PMM1 is chosen in such a way as to
be a representative time point between two successive events. The implementation of
different projects (i.e., risk mitigation structures) is in due course. These projects
originated (i) as a response to the elaborated (negative) experience of the last event, (ii)
as a result of the resources made available for prevention purposes (i.e., quality of
hazard maps and spatial planning; construction of local structural protection measures)
and (iii) as a consequence of the society’s proactive capacity. Optimized lift or bascule
bridges may result in an effective strategy to decrease the probability of LW getting
clogged (e.g., Lange and Bezzola 2006; Rudolf-Miklau et al. 2011). Other existing
bridge structures may be modified as well (see Figure 7). These adaptations are either
directly installed at the bridge structure ((a), (b), (d), (e)) or are realized in the river
channel in close vicinity to the bridge ((c), (d), (e), (f)).
Figure 7: Structural measures forcing an unscathed transfer of LW at bridges;
design examples within the focus of a useful and technically feasible adaptation of
existing bridges (modified and expanded after Gschnitzer et al. (2014b; in press)).
The effects of such adaption measures, and in particular the solutions (a), (c) and
(f) shown in Figure 7, have been extensively analyzed in the lab at the University of
Innsbruck. In particular, the baffle at the upstream edge of the bridge (a) and the
“venturi-like” change of the channel geometry (f) proved a remarkably decrease of
clogging probabilities, at least under the assumed hydraulic conditions (Gschnitzer et al.
in press; Schmocker and Hager 2011).
16
Another alternative is the construction of retention structures upstream a particular
vulnerable section (e.g., bridge which may be prone to be clogged but cannot be
modified or narrow sections). Rimböck (2003) and Bezzola and Hegg (2008) designed
different rack structures perpendicular to the flow and tested their wood retention
capacity in the laboratory. Other designs have been recently proposed, such as the
bypass channel located at the outer river bend, with a rack parallel to the main flow,
where wood is stored (Schmocker and Hager 2013; Schmocker and Weitbrecht 2013).
Numerical and physical hydraulic models are generally used to design such retention
structures and to identify the most effective installation location along a river (Comiti et
al. 2012; Ruiz-Villanueva et al. 2014d). A careful comparison of benefits and costs is in
any case recommended (Lange and Bezzola 2006; Schmocker and Weitbrecht 2013).
The PMM2 assesses the quality of civil protection plans. More generally, at the
PMM2, society is screened in terms of its potential to mitigate risks by making
extensive use of the acquired knowledge. Hence, existing strategies to displace people
and mobile objects are tested and evaluated. The application of the set of methods
outlined in sections 2-4 may significantly contribute to improve the outcomes at PMM2.
After the onset of a flood event, at the PMM3, discrepancies between actual and
required preparedness become apparent. At this stage, depending on the society’s
capacity to resist, intervention actions seek to minimize adverse effects (i.e.,
deployment of temporary protection measures to reduce direct and indirect losses,
employment of excavators at bridges to avoid LW entrapment, emergency decisions
entailing specific sacrifices for the good of the whole, etc.). On an advanced reflective
level, a society can continuously compare its potential responses with those of other
societies facing the real event and identify hidden shortcomings.
At the PMM4 the spectrum of “net consequences” caused by a flood event is
definitely observable. At this stage it is essential to evaluate the society’s reaction
capacity. Such an experimental trajectory modifies the cultural mindset of a society,
which, in turn, contributes to determine the quality of risk prevention.
6 Summary and concluding remarks
The different methods described in this paper have strengths and limitations. In
contrast to flume experiments, which have been widely used (see section 3), numerical
17
modelling has only recently started and, although new applications and developments
are expected (see section 2), still systematic application remains challenging.
As stressed in this paper, models can be integrated in a framework to define a
powerful and strong procedure to assess LW-related hazards. However, the complexity
of LW dynamics makes it challenging to numerically or experimentally reproduce the
complex interactions of different processes. Therefore, deterministic models and flume
experiments should be applied in an exploratory manner, designing multiple scenarios,
supported by the knowledge of the river system and field observations to improve our
insights of the processes involved and to enhance management design accordingly. In
addition, other approaches, such as Formative Scenario Analysis (FSA) or stochastic
procedures, are essential tools to assess hazards and define management strategies. We
remark that the different methods outlined here should be implemented by experienced,
interdisciplinary teams, and should be combined with other approaches (e.g., LW
monitoring in rivers and torrents).
LW management might be addressed at different spatial scales and should be
adapted to each site and problem. More local solutions described above (i.e. wood
removal, modification of infrastructures, installation of retention structures) should be
part of a suitable catchment-wide approach (i.e., integrated basin management strategy)
and should not undermine ongoing river restoration efforts.
Acknowledgements
We acknowledge the essential support of the project REDES 150153 –
CONICYT. Moreover, we would like to thank Dr. Sven Fuchs for useful discussions
and suggestions in relation to the integral risk management process and to Dr. Francesco
Comiti for useful comments on an earlier version of the manuscript.
References
Abbe, T. and Montgomery, D.R. (2003) Patterns and processes of wood debris
accumulation in the Queets River Basin, Washington. Geomorphology, 51, 81–
107.
Alexander, D.E.(2000) Confronting catastrophe. Harpenden, Terra Publishing, UK.
18
Allen J.B., Smith D.L. (2012) Characterizing the Impact of Geometric Simplification on
Large Woody Debris Using CFD. International Journal of Hydraulic
Engineering, 1(2), 1–14.
Anderson, M.B. (2000) Vulnerability to Disaster and Sustainable Development: A
General Framework for Assessing Vulnerability. p. 11–25 in Pielke R, Jr Pielke
(eds). 2000. Storms (Vol. 1). Routledge, London, UK.
Andreoli, A. Comiti, F. Lenzi, M.A. (2007) Characteristics, distribution and
geomorphic role of large woody debris in a mountain stream of the Chilean
Andes. Earth Surf. Process. Landf., 1692, 1675–1692.
Armanini, A. Larcher, M. Odorizzi, M. (2010) Dynamic impact of steep waves against a
vertical wall. Proceedings of First European IAHR Congress, Edinburgh.
Aufleger, M. (2006) Flussmorphologische Modelle – Grundlagen und
Anwendungsgrenzen. Reports of the Chair of Hydraulic and Water Resources
Engineering, Technical University of Munich, 104, 198-207 (in German).
Badoux, A. Andres, N. Turowski, J.M. (2014) Damage costs due to bedload transport
processes in Switzerland. Nat. Hazards Earth Syst. Sci., 14, 279-294.
Badoux, A., Böckli, M., Rickenmann, D., Rickli, C., Ruiz-Villanueva, V., Zurbrügg, S.,
Stoffel, M. (2015) Large wood transported during the exceptional flood event of
24 July 2014 in the Emme catchment (Switzerland). Proceedings of the Wood in
World Rivers Conference, Padova, Italy, July 2015.
Ballesteros Cánovas, J.A. Stoffel, M. Schraml, K. Corona, C. Gobiet, A. Tani, S.
Sinabell, F. Fuchs, S. Kaitna, R. (2016) Debris-flow risk analysis in a managed
torrent based on a stochastic life-cycle performance. Sci. Total Environ., 557-
558, 142-153.
Benke, A.C. Wallace, J.B. (2003) Influence of wood on invertebrate communities in
streams and rivers: Bethesda, Maryland. Am. Fish. S. S., 37, 149–177.
Bertoldi, W. Welber, M. Mao, L. Zanella, S. Comiti, F. (2014) A flume experiment on
wood storage and remobilization in braided river systems. Earth Surf. Process.
Landf., 39, 804–813.
Bertoldi, W. Welber, M. Gurnell, A.M. Mao, L. Comiti, F. Tal, M. (2015) Physical
modelling of the combined effect of vegetation and wood on river morphology.
Geomorphology, 246, 178–187.
Bezzola, G.R., Hegg, C. (2008). Ereignisanalyse Hochwasser 2005, Teil 2—Analyse
von Prozessen, Massnahmen und Gefahrengrundlagen. Bern, Bundesamt für
19
Umwelt BAFU, Birmensdorf, Eidgenössische Forschungsanstalt WSL: Umwelt-
Wissen Nr. 2508.
Bladé, E., Sánchez-Juny, M., Ruiz-Villanueva, V. (2016). Strategies in the 2D
numerical modelling of wood transport in rivers. Proceeding of the Eight
International Conference on Fluvial Hydraulics. St. Louis, Mo, USA 12-15 July
2016.
Bocchiola, D. Rulli, M.C. Rosso, R. (2006) Transport of large woody debris in the
presence of obstacles. Geomorphology, 76, 166–178.
Bocchiola, D. (2011) Hydraulic characteristics and habitat suitability in presence of
woody debris: a flume experiment. Adv. Water Resour., 34, 1304–1319.
Borga, M. Stoffel, M. Marchi, L. Marra, F. Jakob, M. (2014) Hydrogeomorphic
response to extreme rainfall in headwater systems: Flash floods and debris
flows. J. Hydrol., 518, 194–205.
Bracken, L.J. Turnbull, L. Wainwright, J. Bogaart, P. (2015) Sediment connectivity: a
framework for understanding sediment transfer at multiple scales. Earth Surf.
Process. Landf., 188, 177–188.
Braudrick, C.A. and Grant, G.E. (2000) When do logs move in rivers? Water Resour.
Res., 36, 571–583.
Braudrick, C.A. and Grant, G.E. (2001) Transport and deposition of large woody debris
in streams: a flume experiment. Geomorphology, 41, 263–283.
Braudrick, C. A., G. E. Grant, Y. Ishikawa, and H. Ikeda (1997) Dynamics of wood
transport in streams: A flume experiment. Earth Surf. Processes Landforms, 22,
669–83.
Brooks, A.P. Brierley, G.J. Millar, R.G. (2003) The Long-Term Control of Vegetation
and Woody Debris on Channel and Flood-Plain Evolution, Insights from a
Paired Catchment Study in Southeastern Australia. Geomorphology, 51, 7–29.
Bubeck, P. Aerts, J.C.J.H. de Moel, H. Kreibich, H. (2016) Preface: Flood-risk analysis
and integrated management. Nat. Hazards Earth Syst. Sci., 16, 1005-1010.
Carter, D.A. (1991) Quantified Risk Assessment of Petrochemical Installations and
Pipelines in the United Kingdom. Risk Anal., 11, 385–387.
Cavalli, M. Trevisani, S. Comiti, F. Marchi, L. (2013) Geomorphometric assessment of
spatial sediment connectivity in small Alpine catchments. Geomorphology, 188,
31–41.
20
Collins, B.D. Montgomery, D.R. Fetherston, K.L. Abbe, T.B. (2012) The floodplain
large-wood cycle hypothesis: A mechanism for the physical and biotic
structuring of temperate forested alluvial valleys in the North Pacific coastal
ecoregion. Geomorphology, 139–140, 460–470.
Comiti, F. Andreoli, A. Lenzi, M. Mao, L. (2006) Spatial density and characteristics of
woody debris in five mountain rivers of the Dolomites (Italian Alps).
Geomorphology, 78, 44–63.
Comiti, F., D`Agostino, V., Moser, M., Lenzi, M.A., Bettella, F., Dell´Agnese, A.,
Mazzorana, B., 2012. Preventing wood-related hazards in mountain basins:
From wood load estimation to designing retention structures Interpraevent 12 th
Congress Proceedings, Grenoble, France. pp. 651-662.
Comiti, F. Lucía, A. Rickenmann, D. (2016a) Large wood recruitment and transport
during large floods: a review. Geomorphology, 269, 23–39.
Comiti, F. Righini, M. Nardi, L. Lucìa, A. Amponsah, W. Cavalli, M. Surian, N.
Marchi, L. Rinaldi, M. Borga, M. (2016b) Channel widening during extreme
floods: how to integrate it within river corridor planning? Proceedings of the 13th
Interpraevent Congress, pp. 477–486. Luzern, Switzerland, Internationale
Forschungsgesellschaft Interpraevent / International Research Society
Interpraevent, Klagenfurt.
De Cicco. P.N. Paris, E. Solari, L. (2015) Flume experiments on bridge clogging by
woody debris: the effect of shape of piers. Proceedings of the 36th IAHR World
Congress, pp. 2341-2345. The Hague, The Netherlands.
Diehl, T.H.( 1997) Potential drift accumulation at bridges. Publication No. FHWA-RD-
97-028 U.S. Department of Transportation, Federal Highway Administration
Research and Development, Turner-Fairbank Highway Research Center,
Virginia.
Dudley, S.J. Syndi, J. Craig, J. Fischenich, J.C. Abt, S.R. (1998) Effect of Woody
Debris Entrapment on Flow Resistance. J. Am. Water Resour. Assoc., 34, 1189–
1197.
Faustini, J.M. and Jones, J.A. (2003) Influence of large woody debris on channel
morphology and dynamics in steep, boulder-rich mountain streams, western
Cascades, Oregon. Geomorphology, 51, 187–205.
Fuchs, S. (2009a) Susceptibility versus resilience to mountain hazards in Austria:
paradigms of vulnerability revisited. Nat. Hazards Earth Syst. Sci., 9, 337-352.
21
Fuchs, S. (2009b) Mountain hazards, vulnerability and risk. Habilitation Dissertation at
the University of Innsbruck, Austria, unpublished.
Fuchs, S. and Glade, T. (2016) Vulnerability assessment in natural hazard risk: A
dynamic perspective. Nat. Hazards, 82, 1-5.
Galatioto, N. (2016) Modellierung der Schwemmholzdynamik hochwasserführender
Fliessgewässer. Master Thesis Geographisches Institut der Universität Bern (In
German).
Gems, B. Sendlhofer, A. Achleitner, S. Huttenlau, M. Aufleger, M. (2012) Verklausung
von Brücken – Evaluierung verklausungsinduzierter Überflutungsflächen durch
Kopplung eines physikalischen und numerischen Modells. In: Koboltschnig, G.
Hübl, J. Braun, J. (ed) 12th Congress Interpraevent 2012, pp. 131-142.
Internationale Forschungsgesellschaft Interpraevent / International Research
Society Interpraevent, Klagenfurt.
Gems, B. Wörndl, M. Gabl, R. Weber, C. Aufleger, M. (2014a) Experimental and
numerical study on the design of a deposition basin outlet structure at a
mountain debris cone. Nat. Hazards Earth Syst. Sci., 14, 175-187.
Gems, B. Sturm, M. Vogl, A. Weber, C. Aufleger, M. (2014b) Analysis of damage
causing hazard processes on a torrent fan – scale model tests of the
Schnannerbach Torrent channel and its entry to the receiving water. Digital
Proceedings of the Interpraevent 2014 in the Pacific Rim. Nara, Japan.
Gems. B. Mazzorana, B. Hofer, T. Sturm, M. Gabl, R. Aufleger, M. (2016) 3D-
hydrodynamic modelling of flood impacts on a building and indoor flooding
processes. Nat. Hazards Earth Syst. Sci., 16, 1351-1368.
Gippel, C.J. (1995) Environmental Hydraulics of Large Woody Debris. J. Environ.
Eng.-Asce, 121, 388-395.
Gschnitzer, T. Gems, B. Aufleger, M. Mazzorana, B. Comiti, F. (2013) Physical Scale
Model Tests on Bridge Clogging. In: The Ministry of Water Resources, P.R.
China and International Association for Hydro-Environment Engineering and
Research (ed.) 35th IAHR World Congress. In-house Publishing, ISBN 978-7-
89414-588-8.
Gschnitzer, T. Gems, B. Mazzorana, B. Comiti, F. Aufleger, M. (2014a) On the
evaluation and modelling of driftwood clogging processes in flood related
hazards estimation. In: Lollino, G. Arattano, M. Rinaldi, M. Giustolisi, O.
Marechal, J.C. Gordon, E.G. (ed) Engineering Geology for Society and Territory
22
– Volume 3. River Basins, Reservoir Sedimentation and Water Resources, IAEG
XII Congress, pp. 139-142. Springer International Publishing AG, Torino.
Gschnitzer, T. Gems, B. Aufleger, M. (2014b) Physical scale model tests analysing
constructive and hydraulic measures at bridges for the mitigation of hazards
deriving from wood clogging processes. Report Part 2 as contribution to the
work package “State of the art of the design of hazard and vulnerability proof
bridge structures with respect to wood transport (Contribution to the Guidelines
for determining scenarios to be used for flood risk mitigation)” of the SEDALP
project (Sediment Management in Alpine basins).
Gschnitzer, T. Gems, B. Aufleger, M. (2014c) Laborversuche zu
Schwemmholztransport und einhergehenden Brückenverklausungen und
Erstellung eines Leitfadens zur Berücksichtigung dieser Phänomene in der
Gefahrenzonenplanung. Report for the Department of Hydraulic Engineering of
the Autonomous Province of Bolzano, unpublished. (in German).
Gschnitzer, T. Gems, B. Mazzorana, B. Aufleger, M. (2016) Towards a robust
assessment of bridge clogging processes in flood risk management.
Geomorphology, http://dx.doi.org/10.1016/j.geomorph.2016.11.002
Gurnell, A.M. Piegay, H. Swanson, F.J. Gregory, S.V. (2002) Large wood and fluvial
processes. Freshw. Biol., 47, 601–619.
Gurnell, A.M. (2003) Wood storage and mobility, in: Gregory, S. V, Boyer, K.L.,
Gurnell, A.M. (Eds.), The Ecology and Management of Wood in World Rivers.
Am Fish S S., Bethesda, Maryland.
Gurnell, A.M. (2013) Wood in fluvial systems, in: Shroder, J. Wohl, E. (ed) Treatise on
Geomorphology vol. 9, pp. 163–188, Academic Press, San Diego, California.
Hafs, A.W. Harrison, L.R. Utz, R.M. Dunne, T. (2014) Quantifying the role of woody
debris in providing bioenergetically favorable habitat for juvenile salmon. Ecol.
Model., 285, 30–38.
Harmon, M.E. Franklin, J.F. Swanson, F.J. Sollins, P. Gregory, S.V. Lattin, J.D.
Anderson, N.H. Cline, S.P. Aumen, N.G. Sedell, J.R. Lienkaemper, G.W.
Cromack, K. Jr. Cummins, KW. (1986) Ecology of coarse woody debris in
temperate ecosystems. Adv. Ecol. Res., 15,133–302.
Hartlieb, A. (2012) Modellversuche zur Verklausung von
Hochwasserentlastungsanlagen mit Schwemmholz. Wasserwirtschaft, 6, 15-19.
(in German)
23
Harvey, A.M. (2001) Coupling between hillslopes and channels in upland fluvial
systems: Implications for landscape sensitivity, illustrated from the Howgill
Fells, northwest England. Catena, 42, 225–250.
Heller, V. (2011) Scale effects in physical hydraulic engineering models. J. Hydraul.
Res., 49, 293–306.
Kammerlander, J. Gems, B. Sturm, M. Aufleger, M. (2016) Analysis of flood related
processes at confluences of steep tributary channels and their receiving streams –
2d numerical modelling application. 13th Congress Interpraevent 2016,
Conference Proceedings. pp. 319–326. Internationale Forschungsgesellschaft
Interpraevent / International Research Society Interpraevent, Klagenfurt.
Kienholz, H. Krummenacher, B. Kipfer, A. Perret, S. (2004) Aspects of integral risk
management in practice—considerations with respect to mountain hazards in
Switzerland. Oesterr. Wasser Abfallwirtsch., 56, 43–50.
Lange, D., Bezzola, G.R. (2006) Schwemmholz: Probleme und Lösungsansätze. VAW-
Mitteilungen 188, VAW ETH Zurich. (in German).
Lassettre, N.S. and Kondolf, G.M. (2012) Large woody debris in urban stream
channels: Redefining the problem. River Res. Appl., 28, 1477–1487.
Le Lay, Y.F. Piégay, H. Moulin, B. (2013) Wood Entrance, Deposition, Transfer and
Effects on Fluvial Forms and Processes, Problem Statements and Challenging
Issues. Treatise on Geomorphology Vol. 12, pp. 20–36, Academic Press, San
Diego, California.
Lucía, A. Comiti, F. Borga, M. Cavalli, M. Marchi, L. (2015) Dynamics of large wood
during a flash flood in two mountain catchments. Nat Hazard Earth Syst. Sci., 3,
1643–1680.
Macvicar, B. and Piégay, H. (2012) Implementation and validation of video monitoring
for wood budgeting in a wandering piedmont river, the Ain River (France).
Earth Surf. Process. Landf., 37, 1272–1289.
Mao, L. Burns, S. Comiti, F. Andreoli, A. Urciolo, A. Gavino-Novillo, M. Iturraspe, R.
Lenzi, M.A. (2008) Acumulaciones de detritos leñosos en un cauce de Montaña
de Tierra del Fuego: Analisis de la movilidad y de los efectos
hydromorfologicos. Bosque, 29, 197-211.
Mao, L. Andreoli, A. Iroumé, A. Comiti, F. Lenzi, M.A. (2013) Dynamics and
Management Alternatives of in-Channel Large Wood in Mountain Basins of the
Southern Andes. Bosque, 34, 319-330.
24
Mazzorana, B. Hübl, J. Fuchs, S. (2009a) Improving risk assessment by defining
consistent and reliable system scenarios. Nat. Hazards Earth Syst. Sci., 9, 145-
159.
Mazzorana, B. Zischg, A. Largiader, A. Hübl, J. (2009b) Hazard index maps for woody
material recruitment and transport in alpine catchments. Nat. Hazards Earth
Syst. Sci., 9, 197-209.
Mazzorana, B. and Fuchs, S. (2010a) Fuzzy Formative Scenario Analysis for woody
material transport related risks in mountain torrents. Environ. Modell. Softw., 25,
1208-1224.
Mazzorana, B. and Fuchs, S. (2010b) A conceptual planning tool for hazard and risk
management. In: Chen, S.C. (ed.) International Symposium Interpraevent in the
Pacific Rim – Taipei, pp. 828-838. Internationale Forschungsgesellschaft
Interpraevent, Klagenfurt.
Mazzorana, B. Comiti, F. Volcan, C. Scherer, C. (2011a) Determining flood hazard
patterns through a combined stochastic-deterministic approach. Nat. Hazards,
59, 301-316.
Mazzorana. B. Zischg. A. Largiader, A. Hübl, J. (2011b) Modelling woody material
transport and deposition in alpine rivers. Nat. Hazards, 56, 425-449.
Mazzorana, B. Comiti, F. Scherer, C. Fuchs, S. (2012) Developing consistent scenarios
to assess flood hazards in mountain streams. J. Environ. Manage., 94, 112-124.
Mazzorana, B. Levaggi, L. Keiler, M. Fuchs, S. (2013) Towards dynamics in flood risk
assessment. Nat. Hazards Earth Syst. Sci., 12, 3571-3587.
Merten, E.C. Finlay, J. Johnson, L. Newman, R. Stefan, H. Vondracek, B. (2010)
Factors influencing wood mobilization in streams. Water Resour. Res., 46,
W10514.
Merz, B., Hall, J., Disse, M., Schumann, A. (2010) Fluvial flood risk management in a
changing world. Natural Hazards and Earth System Sciences, 10, 509–527.
https://doi.org/10.5194/nhess-10-509-2010.
Mikuś, P. Wyżga, B. Ruiz-Villanueva, V. Zawiejska, J. Kaczka, R.J. Stoffel, M. (2016)
Methods to assess large wood dynamics and the associated flood hazard in
Polish Carpathian watercourses of different size. In: Kundzewicz, Z.W.
Niedźwiedź, T. Stoffel, M. Wyżga, B. (ed) Flood Risk in the Upper Vistula
Basin. Springer, Berlin Heidelberg.
25
Nowacki, R. and Bachnik, K. (2016) Innovations within knowledge management. J.
Bus. Res., 69, 1577-1581.
Ravazzolo, D. Mao, L. Picco, L. Lenzi, M.A. (2015) Tracking log displacement during
floods in the Tagliamento River using RFID and GPS tracker devices.
Geomorphology, 228, 226–233.
Renn, O. (2008) Concepts of risk: an interdisciplinary review. Part 2: integrative
approaches. Gaia, 17, 196-204.
Rickenmann, D. (1997) Schwemmholz und Hochwasser. Wasser Energ. Luft, 89, 115-
119. (in German).
Rickenmann, D., Waldner, P., Usbeck, T., Köchil, D., Sutter, F., Rickli, C., Badeaus, A.
(2015) Large wood transported during the 2005 flood events in Switzerland.
Proceedings of the Wood in world rivers Conference, Padova, Italy, July 2015.
Rigon, E. Comiti, F. Lenzi, M.A. (2012) Large wood storage in streams of the Eastern
Italian Alps and the relevance of hillslope processes. Water Resour. Res., 48,
W01518.
Rimböck, A. (2003) Schwemmholzrückhalt in Wildbächen. [Driftwood retention in
torrents]. PhD Thesis, TU Munich (in German).
Rudolf-Miklau, F., Hübl, J., Schattauer, G., Rauch, H.P., Kogelnig, A., Habersack, H.,
Schulev-Steindl, E. (2011) Handbuch Wildholz – Praxisleitfaden. Internationale
Forschungsgesellschaft Interpraevent, Klagenfurt (in German).
Ruiz-Villanueva, V. Bodoque, J.M. Díez-Herrero, A. Eguibar, M.A. Pardo-Igúzquiza,
E. (2013) Reconstruction of a flash flood with large wood transport and its
influence on hazard patterns in an ungauged mountain basin. Hydrol. Process.,
27, 3424–3437.
Ruiz-Villanueva, V. Bladé, E. Sánchez-Juny, M. Marti-Cardona, B. Díez-Herrero, A.
Bodoque, J.M. (2014a) Two-dimensional numerical modeling of wood transport.
J. Hydroinform., 16, 1077–1096.
Ruiz-Villanueva, V. Díez-Herrero, A. Ballesteros, J.A. Bodoque, J.M. (2014b) Potential
Large Woody Debris Recruitment due to Landslides, Bank Erosion and Floods
in Mountain Basins: A Quantitative Estimation Approach. River Res. Appl., 97,
81-97.
Ruiz-Villanueva, V. Bladé Castellet, E. Díez-Herrero, A. Bodoque, J.M. Sánchez-Juny,
M. (2014c) Two-dimensional modelling of large wood transport during flash
floods. Earth Surf. Process. Landf., 39, 438–449.
26
Ruiz-Villanueva, V. Bodoque, J.M. Díez-Herrero, A. Bladé, E. (2014d) Large wood
transport as significant influence on flood risk in a mountain village. Nat.
Hazards, 74, 967–987.
Ruiz-Villanueva, V. Piégay, H. Gurnell, A.M. Marston, R.A. Stoffel, M. (2016a) Recent
advances quantifying the large wood cycle and dynamics in river basins: new
methods, remaining challenges. Rev. Geophys., 54, 611-652.
Ruiz-Villanueva, V. Wyzga, B. Zawiejska, J. Hajdukiewicz, M. Stoffel, M. (2016b)
Factors controlling large-wood transport in a mountain river. Geomorphology,
272, 21–31.
Ruiz-Villanueva, V. Wyżga, B. Hajdukiewicz, H. Stoffel, M. Wyzga, B. Stoffel, M.
(2016c) Exploring large wood retention and deposition in contrasting river
morphologies linking numerical modelling and field observations. Earth Surf.
Process. Landf., 41, 446–459.
Ruiz-Villanueva, V. Wyżga, B. Zawiejska, J. Mikuś, P. Hajdukiewicz, H.
Hajdukiewicz, M. Stoffel, M. (2016d) Large Wood Transport, Deposition and
Remobilization During Floods in the Czarny Dunajec River: Outcomes from
Numerical Modelling. In Polish Carpathian watercourses of different size. In:
Kundzewicz, Z.W. Niedźwiedź, T. Stoffel, M. Wyżga, B. (ed) Flood Risk in the
Upper Vistula Basin. Springer, Berlin Heidelberg.
Ruiz-Villanueva, V. Wyżga, B. Mikuś, P. Hajdukiewicz, H. Stoffel, M. (2016e) The
role of flood hydrograph in the remobilization of large wood in a wide mountain
river. J. Hydrol., 541, 330–343.
Ruiz-Villanueva, V. Wyżga, B. Mikuś, P. Hajdukiewicz, M. Stoffel, M. (2017) Large
wood clogging during floods in a gravel-bed river: the Długopole bridge in the
Czarny Dunajec River, Poland. Earth Surf. Process. Landf., . DOI:
10.1002/esp.4091.
Schalko, I. Schmocker, L. Weitbrecht, V. Boes, R.M. (2016) Modeling the effect of
organic fine material in a driftwood accumulation on backwater rise. In:
Constantinescu, G. Garcia, M. Hanes, D. Chapman, D. (ed.) Proceedings of
River Flow Conference 2016, pp. 2326-2332. St. Louis, USA.
Schmocker, L. and Hager, W.H. (2011) Probability of Drift Blockage at Bridge Decks.
J. Hydraul. Eng., 137, 470-479.
Schmocker, L. and Hager, W.H. (2013) Scale Modeling of Wooden Debris
Accumulation at a Debris Rack. J. Hydraul. Eng., 139, 827–836.
27
Schmocker, L. and Weitbrecht, V. (2013) Driftwood: Risk Analysis and Engineering
Measures. J. Hydraul. Eng., 139, 683–695.
Schmocker, L. Brändli, D. Weitbrecht, V. Boes, R. (2015) Backwater rise due to
driftwood accumulations. E-proceedings 36th IAHR World Congress. pp. 4–11.
Schöberl, F. (1979) Zur Frage der Gefällsausbildung beim Selbststabilisierungsprozess
von erodierenden Fluß-Strecken. Doctoral Thesis, Unit of Hydraulic
Engineering, University of Innsbruck. (in German)
Scholz, R. and Tietje, O. (2002) Embedded case study methods. London, Sage.
Sendlhofer, A. (2010) Systematische Versuchsreihen zur Überprüfung der
Verklausungssicherheit von Brücken. University of Innsbruck, Unit of Hydraulic
Engineering, Diploma Thesis. (in German).
Steeb, N. Rickenmann, D. Badoux, A. Rickli, C. Waldner, P. (2017) Large wood
recruitment processes and transported volumes in Swiss mountain streams
during the extreme flood of August 2005. Geomorphology, 279, 112–127.
http://doi.org/10.1016/j.geomorph.2016.10.011
Surian, N. Righini, M. Lucía, A. Nardi, L. Amponsah, W. Benvenuti, M. Borga, M.
Cavalli, M. Comiti, F. Marchi, L. Rinaldi, M. Viero, A. (2016) Channel response
to extreme floods: Insights on controlling factors from six mountain rivers in
northern Apennines, Italy. Geomorphology, 272, 78-91.
Swanson, F.J., Lienkaemper, G.W., Sedell, J.R. (1976) History, physical effects, and
management implications of large organic debris in western Oregon streams.
U.S. For. Serv. Gen. Tech. Rep. PNW-56.
Tacnet, J.M. Mermet, E. Maneerat, S. (2012) Analysis of road networks exposed to
natural hazards In: Proceedings of AGILE2012, 15th international conference on
geographic information science proceedings, April 24-27, 2012, pp. 370–375.
Avignon, France.
Tietje, O. (2005) Identification of a small reliable and efficient set of consistent
scenarios. Eur. J. Oper. Res., 167, 418-432.
Versini, P.A. Gaume, E. Andrieu, H. (2010) Application of a distributed hydrological
model to the design of a road inundation warning system for flash flood prone
areas. Nat. Hazards Earth Syst. Sci., 10, 805–817.
Webb, C.B. Barfuss, S.L. Johnson, M.C. (2010) Modelling roughness in scale models.
J. Hydraul. Res., 48, 260–264.
28
Welber, M. Bertoldi, W. Tubino, M. (2013) Wood dispersal in braided streams: Results
from physical modeling. Water Resour. Res., 49, 7388–7400.
Wilcox, A.C. and Wohl, E. (2006) Flow resistance dynamics in step-pool channels: 1.
Large woody debris and controls on total resistance. Water Resour. Res., 42,
W05418.
Wisner, B., Blaikie, P., Cannon, T., Davis, I. (2004) At Risk: natural hazards, people’s
vulnerability and disasters. Routledge, London.
Wohl, E. and Goode, J.R. (2008) Wood dynamics in headwater streams of the Colorado
Rocky Mountains. Water Resour. Res., 44, 1–14.
Wohl, E. and Jaeger, K. (2009) A conceptual model for the longitudinal distribution of
wood in mountain streams. Earth Surf. Process. Landf., 34, 329–344.
Wohl, E. (2013) Floodplains and wood. Earth-Sci. Rev., 123, 194–212.
Wohl, E. (2014) A legacy of absence: Wood removal in US rivers. Progress in Physical
Geography, 38(5), 637–663. https://doi.org/10.1177/0309133314548091
Wohl, E. (2017) Bridging the gaps: An overview of wood across time and space in
diverse rivers. Geomorphology, 279, 3–26.
http://dx.doi.org/10.1016/j.geomorph.2016.04.014
Wohl, E. Bledsoe, P.B. Fausch, K.D. Kramer, N. Bestgen, K.R. Gooseff, M.N. (2016)
Management of Large Wood in Streams: An Overview and Proposed
Framework for Hazard Evaluation. Am. Wat. Res., 52, 315–335.
Young, W. (1991) Flume study of the hydraulic effects of large woody debris in
lowland rivers. Regul. River., 6, 203-211.
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Length excluding references: 5625 words
Figures
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Figure 1: Simulation result for the December 1997 flood in Cabrera Stream
(Spain): scenario without (A and C) wood and with wood transport (B and D). Brown
lines represent logs. Color scale shows water depth (in meters) and flow velocity (in
meters per second). Numbers refer to water depth at specific cross sections. Flow
direction from bottom to top.
Figure 2: Conceptual models based on numerical modelling and field
observations in the Czarny Dunajec River (Poland; after Ruiz-Villanueva et al. 2016b;
2016c; 2016e): (A) Relationship between log volume (in m3 and based on logs with
different sizes) and wood transport (defined as the ration between pieces transported
downstream the studied reach divided by the total inlet logs); (B) Relationship between
discharge and wood transport; (C) Relationship between the flood hydrograph and wood
flux.
30
Figure 3: Front and plan view of the laboratory flume at the University of
Innsbruck; points 1-15 denote to ultrasonic measurements of water level; relevant scale
factors according to Froude similarity for scale models of open channel flow and
experiments with LW clogging at bridges; (adopted from Schöberl (1979) and
Gschnitzer et al. (2014c)).
Figure 4: Damming caused by a congested input of LW (10 pieces each with
branches); initial flow conditions with water levels at lower bridge deck level (at the
upstream edge of the bridge) and channel gradients I of 0.07 % (left), 0.38 % (middle)
and 0.80 % (right); flow direction from left to right (Gschnitzer et al. 2013; 2014c).
31
Figure 5. Reduced consistency matrix based on the variable levels listed in Table
1.
32
Figure 6. Continuum of the LW-related flood risk mitigation trajectory – circular
representation with identification of the planning-management milestones PMMs.
Figure 7: Structural measures forcing an unscathed transfer of LW at bridges;
design examples within the focus of a useful and technically feasible adaptation of
existing bridges (modified and expanded after Gschnitzer et al. (2014b; in press)).
Tables
Table 1. Consistent evolution scenarios ranked in decreasing order of
multiplicative consistency measures MCM.N. Control variables MCM
33
1 US1,2 US2,2 DS1,1 DS2,1 IC1,2 IC2,2 IC3,1 IC4,3 IC5,1 IC6,1 AD1,1 AD2,1 AD3,1 323
2 US1,2 US2,2 DS1,1 DS2,1 IC1,2 IC2,2 IC3,1 IC4,3 IC5,1 IC6,1 AD1,1 AD2,2 AD3,1 322
3 US1,2 US2,2 DS1,1 DS2,1 IC1,2 IC2,2 IC3,1 IC4,3 IC5,1 IC6,1 AD1,1 AD2,1 AD32 320
4 US1,2 US2,2 DS1,1 DS2,1 IC1,2 IC2,2 IC3,1 IC4,3 IC5,1 IC6,1 AD1,1 AD2,3 AD3,1 316
5 US1,2 US2,2 DS1,1 DS2,1 IC1,2 IC2,2 IC3,1 IC4,3 IC5,1 IC6,1 AD1,1 AD2,2 AD3,2 315
6 US1,2 US2,2 DS1,1 DS2,1 IC1,2 IC2,2 IC3,1 IC4,3 IC5,1 IC6,1 AD1,1 AD2,3 AD3,2 314
34