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, bruno.mazzorana@uach.cl

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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.

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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