potential large woody debris recruitment due to ...

RIVER RESEARCH AND APPLICATIONS

River Res. Applic. 30: 81–97 (2014) Published online 17 October 2012 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/rra.2614

POTENTIAL LARGE WOODY DEBRIS RECRUITMENT DUE TO LANDSLIDES, BANK EROSION AND FLOODS IN MOUNTAIN BASINS: A QUANTITATIVE ESTIMATION APPROACH V. RUIZ-VILLANUEVAa*, A. DÍEZ-HERREROa, J. A. BALLESTEROSa AND J. M. BODOQUEb a

b

Department of Research and Geoscientific Prospective, Geological Survey of Spain (IGME), Ríos Rosas 23, Madrid E-28003, Spain Mining and Geological Engineering Department, University of Castilla-La Mancha, Campus Fábrica de Armas, Avda. Carlos III, Toledo, Spain

ABSTRACT In-depth knowledge of the fluvial corridor and surrounding slopes and forest vegetation is needed for a better understanding of wood recruitment or inputs to rivers. The information available in Central Spain on hydrogeomorphic processes and forest distribution enabled the evaluation of potential wood recruitment from three sources: landslides, bank erosion and fluvial transport during floods on a regional scale. The method presented here is based on a geographical information system (GIS) and on multi-criteria and multi-objective assessment using fuzzy logic principles. First, the areas potentially affected by landslides, bank erosion and floods were delineated, and a vegetation analysis was carried out to obtain the vegetation resistance and forest density. Several scenarios were proposed based on the process frequency and severity. Using this method, the volume of potentially available wood can be estimated for each scenario. Fourteen river basins in populated areas were selected for further analyses and field survey. Observations of in-stream storage of woody debris and tree disturbances were used to interpret the woody debris dynamics throughout the watershed and validate the obtained results. This method offers a suitable approach to define a watershed’s capacity to recruit wood material to streams by delineating the source areas and estimating the order of magnitude of the wood volume in each case. The results may be useful to characterize the dynamics of woody debris from the perspective of the potential hazard of its transport during floods, and they can also be used for forest and river management and restoration. Copyright © 2012 John Wiley & Sons, Ltd. key words: large woody debris; wood recruitment; hydrogeomorphic processes; tree disturbance Received 30 March 2012; Revised 1 August 2012; Accepted 5 September 2012

INTRODUCTION Mountainous regions are characterized by complex dynamics due to the action of multiple hydrogeomorphic processes, such as mass movements on hillslopes, flows, and floods. These processes involve transformations influenced by the amount of water and the other materials it transports, such as sediment and large woody debris (LWD). In forested mountain catchments, the supply of LWD, i.e. wood pieces of 1 m or more in length or 0.1 m or more in diameter, may be caused by a variety of mechanisms including mass wasting, channel migration and bank undercutting (May and Gresswell, 2003; Swanson, 2003; Figure 1), stochastic mechanisms such as windthrow and fire (Benda and Sias, 2003; Rosso et al., 2007) or simple tree mortality (Benda and Sias, 2003). Vegetation affected but not killed by hydrogeomorphic processes will react to these disturbances (Stoffel and

*Correspondence to: V. Ruiz-Villanueva, Department of Research and Geoscientific Prospective, Geological Survey of Spain (IGME), Ríos Rosas 23, Madrid E-28003, Spain E-mail: [email protected]

Copyright © 2012 John Wiley & Sons, Ltd.

Wilford, 2012) and may also provide information on the river dynamics (i.e. dendrogeomorphology) and LWD. Trees can be transported or recruited to streams through hillslope failure, landslides and other slope processes which may convey (by sliding or rolling) standing and lying woody material within the affected area towards the river. Some toppled trees further from the channel may not reach the stream directly but can destabilize other trees closer to them. Within the stream bed and floodplain, erosion processes occur along the banks, altering the static equilibrium of trees, so these may topple or slide into the channel. This process is related to the streamflow power and its geomorphological configuration. Woody material is also available and mobile within the channel bed and on forested bars and may be transported and deposited by fluvial mechanisms. The presence of wood in streams has been shown to be linked to positive ecological effects, in contrast to past perceptions that streams need to be freed from obstructions (see references in Kasprak et al., in press). A better understanding of LWD entrainment, or the process by which woody material is transported to the river, is therefore needed when considering the effects of LWD in rivers from an ecological perspective, for analysing geomorphological processes and

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and fluvial transport during floods (Figure 1). Other factors such as wind, snow, fires or decay that may also deliver woody material are outside the scope of this study. Since these geomorphological processes often occur during major floods (Nakamura et al., 2000) when the number of wood pieces likely to be transported increases, the recruitment and dynamics of woody material have been studied from the perspective of potential responsibility in increasing the impact of flood hazards (see examples in Comiti et al., 2008; Mao et al., 2010; Rickenmann and Koschni, 2010; Mazzorana et al., 2011; Ruiz-Villanueva et al., 2012, under review).

METHODOLOGY Figure 1. The three main wood recruitment processes on a catchment scale studied in this analysis: (Ls) landslides; (Ft) fluvial transport during floods; (Be) stream bank erosion. This figure is available in colour online at wileyonlinelibrary.com/journal/rra

for flood hazard assessment. Therefore, the rate of LWD delivered to streams has been the subject of several studies in recent years. Martin and Benda (2001) constructed an LWD budget using a proposed quantitative framework to evaluate spatial and temporal controls on LWD recruitment rate transport. Some years later, Benda and Sias (2003) evaluated the mass balance of in-stream organic debris, making quantitative estimates of wood flux. May and Gresswell (2003) identified wood recruitment and redistribution mechanisms during a retrospective investigation. Bragg and Kershner (2004) analysed reach-scale tree recruitment based on bank erosion and tree fall patterns. Mazzorana et al. (2009) proposed a procedure based on empirical indicators to determine the relative propensity of mountain streams to recruit woody material. At a watershed scale, Kasprak et al. (in press) developed a method using LIDAR data to evaluate potential wood recruitment. Based also on raster analyses, Mazzorana et al. (in press) estimated absolute volumes of recruited material. Nevertheless, a quantitative estimation of LWD volume (in terms of number of trees) and the definition of contributing areas based on different processes and levels of severity had not been achieved so far. Therefore, the aims of this paper are (i) to define the areas that may contribute woody material to streams, observing the importance of different recruitment processes and creating reliable scenarios based on the process severity; (ii) to provide estimates of the order of magnitude of recruitable wood volumes for each scenario; and (iii) to study the LWD dynamics in those catchments identified as potentially risk prone. Because the focus here was on the role of hydrogeomorphic processes in supplying woody material to the channel, a spatially inclusive approach is adopted regarding the volume of potentially recruitable wood from landslides, bank erosion Copyright © 2012 John Wiley & Sons, Ltd.

The proposed method is divided into three main steps (Figure 2): (i) terrain analysis to establish wood sources, such as areas affected by landslides, floods and bank erosion, in addition to taking into account the different scenarios (based on the process severity); (ii) vegetation analysis to obtain the tree resistance and forest density; and (iii) wood volume estimates for each scenario. Finally, the results are evaluated with a field survey. The whole analysis is based on a GIS combining morphometric information derived from sources such as digital elevation models (DEM; 25 m pixel size), topographic maps (BCN25, 2011; 1:25 000 scale), existing available hazard maps (Diez-Herrero and Ballesteros, 2009; province of Ávila scale), geological and geomorphological spatial information (GEODE, 2011; 1:50 000 scale), forestry maps and a detailed forest inventory data base (MFE, 2011; 1:25 000 scale). Terrain analysis: potential wood source areas Landslide susceptibility and stream connectivity. The importance of wood recruitment due to mass wasting depends on the type and size or area of the landslide, the age or size of trees recruited and the connectivity to the channel (source areas intersecting a channel segment of a given length; Benda and Sias, 2003). Existing and published natural hazard maps for the province of Ávila (Díez-Herrero and Ballesteros, 2009; Ballesteros-Cánovas et al., 2012) were used to establish potential landslide areas, as they include landslide susceptibility information. A buffer area of influence was also established around these areas, to include toppled trees that may be recruited indirectly by the action of landslides. A number of studies have shown the importance of tree proximity to the channel on overall recruitment (McDade et al., 1990; Robison, 1990; Van Sickle and Gregory, 1990; Bragg and Kershner 2004). Therefore, the connectivity between those areas and the stream was analysed, as this is decisive in the final wood entrainment. Van Sickle and River Res. Applic. 30: 81–97 (2014) DOI: 10.1002/rra

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RECRUITABLE LARGE WOODY DEBRIS IN MOUNTAIN BASINS

Figure 2. General methodology flowchart.

Gregory (1990) quantified the probability of a tree entering the stream as a function of the distance from the nearest channel boundary and the tree height (Ht); Mazzorana et al. (2009) also took the slope gradient into account. Connectivity was established as a function of both the distance to the channel and the slope. If a tree is located in a landslide-prone area or in the toppling influence area, then it will reach the channel if it is at a close enough distance [D; D = kHt, where k is the toppling coefficient and the value assigned here was 2 (tree height 2), \or if it is further away but on a steep slope (>40%)]. As a first step, the landslide susceptibility is categorized, and then the connectivity is analysed. Then, the recruitment probability is assigned for each possible combination

(i.e. high susceptibility and high connectivity area will have a recruitment probability > 50%, and so on; see Figure 3). Flood severity and frequency. Trees located on the riverbed, forested bars or flood plains will be easily entrained during floods. Flood risk and severity assessment is difficult on a regional scale because of the considerable amount of accurate data required for hydrologic and hydraulic studies. However, this information is available for the province of Ávila (Díez-Herrero and Ballesteros, 2009; BallesterosCánovas et al., 2012). The hypothesis used was that, in flood-prone areas, the fluvial transport of wood will be intense and that the recruitment due to flood events will be an important entrainment process.

Figure 3. Probability (in percentage) that wood will be recruited from the hillslopes based on connectivity to the channel. Copyright © 2012 John Wiley & Sons, Ltd.

River Res. Applic. 30: 81–97 (2014) DOI: 10.1002/rra

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Detailed geological and geomorphological information was used to delineate those areas (digital geological map of Spain (GEODE) and topographic and geomorphological maps). From these maps, units related to fluvial processes of relatively recent genesis (Quaternary, preferably Holocene deposits) were selected. Where possible, areas with current activity and with different flooding frequency were chosen as potential flood-prone areas using the methodology proposed by the National Flood Zone Mapping System (SNCZI) in the preliminary flood risk assessment (MAGRAMA, 2012) following the European Union Floods Directive (Directive 2007/60/EC of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risks). Bank erosion susceptibility. Bank erosion often occurs during floods, and it recruits trees at rates depending on the erodibility of banks, flood frequency and stand density (Benda and Sias, 2003). Following Nakamura and Swanson (1994) and Kasprak et al. (in press), the hypothesis put forward here was that the measurement of channel sinuosity quantifies how prone the stream is to lateral migration. Transport capacity is also used here as an indicator of the potential stream capacity for bank erosion. Empirical equations have been proposed for steep mountain streams to estimate orders of magnitude for sediment concentration (Smart and Jaeggi (1983); Mizuyama and Shimohigashi (1985); Bathurst et al. (1987); Meunier (1989); Rickenmann (1990 and 1991)). This concentration is the ratio between solid and liquid discharges, and it can be

used as an indicator of stream transport capacity. In general, these are regression equations based on field and laboratory data and related to stream gradient. According to our hypothesis, stream reaches with high sinuosity (high lateral migration of the channel) and high transport capacity (high sediment concentration) will be prone to bank erosion. Therefore, the reasoning is as follows: trees located on the riverbed or flood plains (defined in the previous subsection) will be easily entrained during floods, and bank erosion will significantly increase the entrainment probability (Figure 4). Vegetation analysis and available wood volumes The vegetation analysis is divided into two steps: (i) vegetation resistance is defined and established for the entire area; (ii) the potential recruitable wood volume is calculated. Data source of vegetation resistance. Vegetation resistance– resilience has been defined previously (see Mazzorana et al., in press), but this term is used here to define the extent to which vegetation can be recruited. This depends on both stand structure and species-specific characteristics. The Forest Map of Spain and National Forest Inventory produced by the Ministry of the Environment, Spain (MFE, 2011), include 15 different descriptor fields for the ecology and structure of the forest mass. Up to three different species of forest trees are identified, each with their development stage, percentage occupation (percentage of the total forest occupied by this species), canopy cover for total trees (percentage of ground covered by the horizontal projection of the canopy) and diameter at breast height, among others.

Figure 4. Probability in percentage of wood recruited within the fluvial corridor based on flood frequency, flood severity and stream bank

erosion capacity. Copyright © 2012 John Wiley & Sons, Ltd.


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In general terms, the vegetation resistance increases when the area has been reforested or increasingly managed (Hutte, 1968; Stumbles, 1968). Given that planted riparian vegetation can take decades to mature fully, it is assumed that the natural rate of LWD recruitment would be significantly reduced in rehabilitated riparian corridors in comparison to remnant corridors (Webb and Erskine, 2003). The type of tree (conifer, deciduous or riparian) is also relevant to this resistance because an abundant below-ground root biomass anchors the tree to the substrata. In riparian vegetation, the root system depth depends on the water table, whereas in conifers, it may be deeper, and therefore, the resistance to toppling may be greater (Naka, 1982; Abernethy and Rutherfurd, 2001). This means that the recruitment potential will increase with trees in their natural state, and even more so if they are riparian species. Volume of potential recruitable wood. The identification and spatial delimitation of possible woody material recruitment areas have been defined in the previous sections. A description follows of how the potentially recruitable wood volume was estimated. ‘Potentially recruitable’ is used here as the maximum number of trees that can contribute wood to the channel throughout the sub-basin. Once the source areas are defined and classified into different categories, the number of trees from each area is estimated and a statistical analysis is performed. The National Forest Map and Inventory provided the required data; however, some simplification had to be assumed. Data were provided for the three main species in any given area and the total canopy cover (Ci) as a percentage of the total area covered by forest. The inventory for the province of Ávila contains estimates of tree density (expressed as number of trees per area) for each species for the whole forested territory. This latter density is called here relative density per species (RDSPi) and is used together with the species occupation and canopy cover to estimate final volumes in a given area. Ai is the contributing area defined for a specific recruitment process and with an established occurrence probability or severity: PRWSPi ¼ Ai Ci RDSPi

(1)

To take into account the defined vegetation resistance and the severity of the potential recruitment mechanism, a volume correction factor (Fc) was defined, which is equivalent to a recruitment probability. As shown in Figures 3 and 4, this can be 1, 0.5 or 0.1. This factor therefore reduces the total volume of potential recruitable wood in those areas where susceptibility to the process is lowest and/or vegetation resistance is highest. PRWti ¼ PRWSPi Fc

PRWt ¼ PRWls þ PRWbe þ PRWft

(3)

where PRWsl is the potentially recruitable wood volume due to landslides, PRWbe is the potentially recruitable wood volume due to bank erosion and PRWft is the potentially recruitable wood volume due to fluvial transport during floods. Scenario definition Different scenarios were created based on process frequency and severity. Then, the potential source areas were defined and classified using fuzzy associative matrices, and the potentially available recruitable wood volumes were also estimated based on the forest data for each scenario: • Scenario 1: Maximum recruitment: all processes (landslides, floods and bank erosion) take place and affect all prone areas (defined at all severity levels). This scenario is established as unlikely to occur. • Scenario 2: Intermediate recruitment: all processes take place but only in those areas assigned a high severity; the probability of this scenario in the area is low–medium. • Scenario 3: Likely recruitment: only high severity floods and bank erosion recruit woody material, and landslides do not take place. This probability of this scenario in the area is medium–high, where floods occur fairly often, so it is hypothesized as the most reliable case. The classification of potential wood source areas was based on fuzzy logic principles (Zadeh, 1965 and 1968; Zimmermann, 2001), which usually use IF–THEN rules such as fuzzy associative matrices (Appendix A). In fuzzy logic applications, non-numeric linguistic variables are often used to facilitate the expression of rules and facts. A linguistic variable may have a value or its antonym, or something in between, and they are useful because they can be modified using linguistic hedges applied to primary terms. This procedure allows the areas to be classified by the likelihood that they will recruit wood material based on the severity of the potential recruitment process and the vegetation resistance and abundance. Fuzzy associative matrices take into account three categories or impact levels (high, medium and low) for all factors (process severity or susceptibility, vegetation resistance, correction factor, etc.). In those basins where a river flows through a populated area, scenarios were established, and the potentially recruitable wood volume was calculated. Statistical analyses (using the R programming language, www.r-project.com) were carried out to interpret the results, and this procedure allowed us to compare the data of the different selected basins. Field observations

(2)

Finally, the total wood volume is

Field observations of in-stream woody debris storage and tree disturbances were used to evaluate the results and



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interpret the dynamics of woody debris throughout the selected watershed (Figure 5). To do this, a visual assessment of the probable source of wood recruitment or process was attempted. Solid material (sediment and wood) transported in the streamflow may damage trees, and this is the basis of dendrogeomorphology, which uses tree-ring analysis to interpret geomorphic processes (Stoffel and Bollschweiler, 2008; Ruiz-Villanueva et al., 2010; Ballesteros Cánovas et al., 2011). Evidence from surviving trees may be used to interpret LWD dynamics, which can be particularly useful when woody debris has been removed from the river or transported over longer distances. Díez-Herrero et al. (in press) proposed a unified classification for tree disturbances caused by floods, and here, these disturbances are related to wood recruitment, delivery and transport. For instance, candelabra growth (FDE 4), decapitated trees (FDE 6), scars (FDE 7 and 9), branches torn off (FDE 8), narrowing in trunks (FDE 12) and bifurcations (FDE 13) may be related to LWD delivery and transport, whereas vegetation patterns (FDE 1, 2, 3), tilted trees (FDE 5) or exposed roots (FDE 17 and 18) are evidence of LWD recruitment and potential delivery. The selected basins were surveyed in order to find direct and indirect evidence of LWD dynamics focusing on recent landslide activity, riverbank erosion, high flood frequency (i.e. high water marks, palaeostage indicators, etc.), high in-stream wood storage and external disturbances in living trees. These observations were compared with the results obtained in the wood volume estimations.

TEST SITE The study site is in the central and eastern massif of Sierra de Gredos, the highest section (Almanzor Peak, 2592 m a.s.l.) of the Spanish Central System, which crosses the Iberian Peninsula from SW to NE. The sector of Sierra de Gredos studied here is in the south of the province of Avila, where

the information required for the analysis was available; the total area of the study site is 4658 km2. Sierra de Gredos is mainly composed of granite tectonic blocks sloping towards the north and separated by a network of fault lines, running N–S and E–W. The bedrock consists of Upper Palaeozoic granitoids. Regolith covers most of the slopes in Sierra de Gredos, reaching a depth of 3–4 m in some places. The weathering mantle is not firmly anchored to the slopes, so heavy rains weaken it and initiate shallow landslides and debris flows (Palacios et al., 2003; Ruiz-Villanueva et al., 2011). This tectonic history determines the most important geomorphologic characteristic of Sierra de Gredos: a steep southern wall (with a 2000-m drop in 8 km) cut only by short ravines and a gentler northern slope, broken by longer, deeper valleys. These mountains are the natural division between the basins of the rivers Tagus and Duero. The area studied here is surrounded by the main tributaries of these two important rivers: the Tiétar to the S, the Adaja to the NE, the Tormes to the NW, the Alberche to the E and the Jerte to the W (Figure 6). The climate of the study area is Continental Mediterranean. This climate is determined by the frequent arrival of Atlantic depressions from the SW during autumn, winter and spring and by the predominant Azores anticyclone causing very dry summers (only 10% of annual precipitation). On the northern slope, the mean annual precipitation is 554 mm at 1007 m a.s. l. and 1704 mm at 1200 m a.s.l. and is estimated to be around 2000 mm at 2000 m a.s.l., of which 77% falls as snow. The south face of Sierra de Gredos creates a formidable orographic barrier to southwesterly storms. Records show that approximately every 3 years, rainfall exceeds 250 mm in 1 week (Palacios et al., 2010). Several factors also determine the occurrence of frequent flash floods. These include the high drainage density, the morphology of the headwaters and the generally small basin areas with a very steep average slope, resulting in very short response times.

Figure 5. Direct and indirect wood recruitment and delivery evidence can be used in the fieldwork to interpret LWD dynamics. HWM, high

water marks; PSI, palaeostage indicators. Copyright © 2012 John Wiley & Sons, Ltd.


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Figure 6. Location of the study site. This figure is available in colour online at wileyonlinelibrary.com/journal/rra

There is abundant forested mass in the area (2358 km2 or 50.6% of the study site), and different species are found depending on the altitude (Figure 7). At higher altitudes, where the extreme climatic characteristics impede the development of trees or shrubs, the main grass species are Festuca sp. and Carex sp. At lower levels, shrubs (Cytisus sp.) and stretches of high mountain conifers such as Pinus sylvestris are present. At lower altitudes, this latter species is replaced by Pinus pinaster, which usually appears with Genista florida, Ilex aquolifolium, Erica arborea and Sorbus aucuparia. Further down, the forest is composed mainly of deciduous trees such as Quercus pyrenaica and Quercus ilex. The vegetation found on the riverbanks is predominantly Alnus glutinosa and Fraxinus angustifolia. Basins with a river flowing through a populated area (assuming potential flood risks) were selected for further analysis and field surveys (Table I) and are highlighted in Figure 7.

RESULTS The obtained results are grouped in three sections: (i) wood contributing areas, (ii) potentially recruitable wood volumes and (iii) field observations. Wood contributing areas due to landslides, bank erosion and fluvial transport during floods The landslide-prone areas according to the stability model results make up around 10% (461 km2) of the study site. Close to 55% of these areas (254 km2) fall into the medium Copyright © 2012 John Wiley & Sons, Ltd.

susceptibility category, and just 30% (138 km2) and 15% (71 km2) are considered to have high and low landslide susceptibility, respectively. A 50-m buffer zone was set around these landslide-prone areas as the toppling influence area. This increases the potential area that could be affected by landslides by 233.11 km2 (695 km2 in total). Seventy-five per cent of this area is very or fairly well connected to the channel, which means that trees in these areas may reach the channel due to landslide recruitment. However, only 158 km2 (32%) of this area is forested according to the National Forest database. From the flood hazard assessment of the whole area, the flood severity for around 10% of the watersheds is classified as very high, 32% as high, 43% as medium and 15% as low. The Quaternary units delineated in relation to flood processes occupy a total area of 610 km2. Of these, 50.5% (308 km2) are stream bed or channels, 10.5% (65 km2) floodplains and bars, 3% (17 km2) terraces, and 36% (221 km2) other formations (fans, etc.). The likelihood of potential recruitment of wood material in these areas due to floods was classified as follows: high, 47 % (288 km2); medium, 15% (88 km2); and low, 38% (232 km2). Only 20% (123 km2) of the defined areas are forested. The analysis of these parameters and variables of each selected basin is shown in Table II. The forested area was classified based on the vegetation resistance following the methodology previously described (see Figure 8). The vegetation resistance to recruitment of the total forested area (2358 km2) is classified as follows: low, 40%; medium, 47%; and high, 13%. River Res. Applic. 30: 81–97 (2014) DOI: 10.1002/rra

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Figure 7. Land use and forested canopy cover of the study region. This figure is available in colour online at wileyonlinelibrary.com/journal/rra

Potentially recruitable wood volume: theoretical predictions After analysing the contributing areas defined for the different recruitment processes, the vegetation resistance and the forest density (three main species, canopy cover and relative density), theoretical predictions were obtained of the number of trees potentially recruitable in each scenario (Table III and Figure 9). Because there may be some uncertainties in the sources used for the analysis, these numbers are taken here as orders of magnitude and used to identify the main recruitment processes and compare basins.

Some observations can be made from the results. In Scenarios 1 and 2, the major recruitment process is slope movements in terms of the number of recruitable trees, except for the basin called Navaluenga 1. There are no major differences between Scenarios 1 and 2 regarding the importance of landslide and fluvial transport during floods, whereas bank erosion seems to be less important in Scenario 2 (except for Navaluenga 1, where it is the main recruitment process together with floods in both scenarios). Based on the interpretation of Figure 9, some basins seem to show a different behaviour in all scenarios. To analyse

Table I. Selected basins and main characteristics Name Arenas Burgohondo Burguillo Candeleda Casavieja Cuevas Guisando La Adrada Mijares Navalacruz Navaluenga 2 Navaluenga 1 Piedralaves Venero

Drainage area (km2)

Main stream length (km)

Basin slope ( )

50.7 25.8 12.0 54.4 10.0 10.0 14.4 20.4 14.7 5.3 30.5 32.2 9.5 15.5

13 12 8 16 5 8 7 8 5 4 5 17 5 5

23 15 18 26 14 23 28 23 21 18 15 19 26 23


Dominant tree species Pinus pinaster Pinus sylvestris Pinus pinaster Quercus pyrenaica Pinus pinaster Juniperus oxycedrus Pinus pinaster Pinus pinaster Pinus pinaster Quercus pyrenaica Quercus pyrenaica Quercus pyrenaica Pinus pinaster Pinus pinaster

Relative density [trees km–2] 9674 3198 9674 5129 9674 1028 9674 9674 9674 5129 5129 5129 9674 9674


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Table II. Defined forested areas, areas prone to landslides, floods and bank erosion (BE) for selected basins. Percentages indicate the ratios between the total forested area and the delineated area defined in the previous column. Name Arenas Burgohondo Burguillo Candeleda Casavieja Cuevas Guisando La Adrada Mijares Navalacruz Navaluenga 1 Navaluenga 2 Piedralaves Venero

Forested area (km2)

Area prone to landslides (km2)

%

High landslide severity (km2)

%

27.03 7.14 8.44 25.62 5.04 5.79 5.71 14.45 5.21 1.48 8.65 15.99 5.00 9.42

5.49 2.70 1.90 13.96 1.93 2.37 2.75 3.17 2.67 0.74 0.30 1.85 2.96 1.56

20 38 22 54 38 41 48 22 51 50 3 12 59 17

2.26 1.01 0.87 7.48 1.63 1.35 1.72 1.58 1.83 0.54 0.24 0.70 2.13 0.78

8 14 10 29 32 23 30 11 35 36 3 4 42 8

this observation in detail, a cluster analysis (hierarchical clustering using Ward’s method) was carried out based on the number of recruitable trees, basin area, forested area, landslide-prone area, flood-prone area and bank erosion-prone area for each scenario (Figure 10). For Scenarios 1 and 2, three different groups were defined, whereas for Scenario 3, two groups were distinguished. This reveals different behaviour between several groups, but highlights similarities between some basins: Arenas, Candeleda, Guisando, La Adrada, Navaluenga 1, Navaluenga 2 and Venero.

Area prone to floods High flood High BE and BE (km2) % severity (km2) % severity (km2) % 5.09 1.92 2.50 4.37 0.18 0.75 1.23 1.22 0.92 0.25 8.04 5.73 0.46 1.49

19 27 30 17 4 13 22 8 18 17 93 36 9 16

3.28 0.15 0.22 1.62 0.08 0.15 0.92 0.42 0.14 0.04 2.86 1.07 0.09 0.40

12 2 3 6 2 3 16 3 3 3 33 7 2 4

0.00 0.11 0.00 0.10 0.00 0.00 0.00 0.09 0.00 0.00 1.69 0.84 0.18 0.00

0 6 0 2 1 0 0 8 0 0 21 15 39 0

Linear regression and graphical correspondence analysis allowed us to evaluate the significance and relationship of these variables with the number of potentially recruitable trees obtained in each scenario (Figure 11). Regression analysis showed a significant relationship between recruitment wood volume and recruitment processes, and the relationship to the basin or forested areas is less significant (p-value = 0.61–0.36). In Scenarios 1 and 2, the landslide-prone area is the most significant variable (p-value = 0.00521 and p-value = 0.000110, respectively),

Figure 8. Vegetation classification based on the resistance to be recruited, and pie chart with areas. This figure is available in colour online

at wileyonlinelibrary.com/journal/rra Copyright © 2012 John Wiley & Sons, Ltd.


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0 35 0 4 0 0 0 17 0 0 42 18 20 0 9842 323 739 4553 461 239 4229 2448 326 65 3047 1064 437 1286 55 6 17 14 5 13 36 21 4 4 52 30 4 32 45 91 83 85 95 87 64 74 96 96 10 64 95 68

0 3 0 1 0 0 0 4 0 0 38 6 1 0

100 65 100 96 100 100 100 83 100 100 58 82 80 100

% Bank erosion % Floods SC3 % Bank erosion % Floods % Landslides

DISCUSSION

41 3 12 11 5 16 30 17 4 3 48 16 3 25

9 3 8 3 2 6 7 8 4 1 41 21 7 12

18 022 3494 4468 30 094 8631 1813 11 695 9554 7254 1511 3376 2957 8580 3992

Large wood recruitment: sources and volumes

24 259 6914 6391 38 605 9470 2946 14 195 12 201 8893 1953 3884 5826 10 471 5313 Arenas Burgohondo Burguillo Candeleda Casavieja Cuevas Guisando La Adrada Mijares Navalacruz Navaluenga 2 Navaluenga 1 Piedralaves Venero

50 94 80 86 93 78 63 74 91 96 11 64 90 63

SC2 % Bank erosion % Floods

The fieldwork allowed us to interpret the results, and certain indicators were used to corroborate the hypotheses proposed (Table IV), although in some cases, they may not be perfectly correlated. Table IV shows that two or more field indicators were found in some basins. These basins (shown in bold) also showed the highest wood volume estimates and were grouped together in the cluster analysis: Arenas, Candeleda, Guisando, La Adrada, Navaluenga 1, Navaluenga 2 and Venero.

The following section discusses the proposed methodology and the results obtained, analyses some errors and limitations and highlights some potential applications.

SC1

% Landslides

whereas in Scenario 3, the most significant variable is the flood-prone area (p-value = 8.19e–06) and the significance of the relationship of the basin area and forested area decreases (lowest p-values = 0.36). Field survey

Name

Table III. Results of potentially recruitable trees obtained for the selected basins. SC1, Scenario 1; SC2, Scenario 2; SC3, Scenario 3.This number is then analysed by percentages based on the recruitment process.



The aim of this paper was to propose a method to define recruitment areas due to landslides, floods and bank erosion, and to estimate the volume of potentially recruitable wood depending on process severity. Other recruitment processes such as wind, snow, fire or chronic wood decay are outside the scope of this study. Wood decay could be included as a percentage of the total wood volume (if data are available) because tree mortality and fall rates can generate some variation in wood recruitment (Benda and Sias, 2003). The whole analysis was based on existing hazard maps, available morphometric information derived from DEMs, geological and geomorphological spatial information and forestry inventory and maps. A GIS was used to obtain a spatially distributed analysis of potential LWD source areas and estimated wood volumes. Multi-criteria and multiobjective evaluation and fuzzy logic principles were used to define reliable scenarios, classifying areas by the likelihood of wood material recruitment based on potential recruitment processes, vegetation resistance and abundance. Fuzzy associative matrices allowed all the available information to be used reliably based on the three categories or impact levels defined. The fuzzy algebraic operations assign consistency to all combinations, and the end result of the procedure is a series of scenarios (similar to the formative scenarios defined by Scholz and Tietje, 2002), which include all the levels previously mentioned. River Res. Applic. 30: 81–97 (2014) DOI: 10.1002/rra


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Figure 9. Number of potentially recruitable trees for the selected 14

basins for (A) Scenario 1, (B) Scenario 2 and (C) Scenario 3.

The potentially recruitable number of trees was successfully estimated for each scenario using this method. ‘Potentially recruitable’ has been defined as the number of trees that may contribute wood to the channel throughout the basin. This concept has been used before (Kasprak et al., in press). However, these authors evaluated the number of vegetation pixels (based on LIDAR DEM) that were tall enough to span the channel, rather than individual trees. The amount of wood in streams (wood budget) was estimated by Martin and Benda (2001), Benda and Sias (2003) and May and Gresswell (2003). Based on in-channel wood volume, they developed simplified mathematical expressions to estimate LWD flux; here, the number of standing trees located in the recruitment source areas that could Copyright © 2012 John Wiley & Sons, Ltd.

Figure 10. Dendograms of hierarchical clustering using Ward’s

method. Dendograms for (A) Scenario 1, (B) Scenario 2 and (C) Scenario 3. River Res. Applic. 30: 81–97 (2014) DOI: 10.1002/rra

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Figure 11. (A, B, C) Graphs of a correspondence analysis for each scenario. Basin, basin area; Veg_Area, forested area; Lnd, landslide-prone area; H_Lnd, high severity landslide-prone area; Ft, flood- and bank erosion-prone area; H_Ft, high severity floodprone area; H_Be, high severity bank erosion-prone area.

reach the streams was computed. Mazzorana et al. (2009) defined an expression to estimate the relative availability of recruitable woody material. However, this is a synthetic indicator for the relative propensity of a basin for entrainment and delivery of woody material under given transport conditions. In a later work, Mazzorana et al. (in press) established using pixels the maximum amount (m3) of transported woody material during a flood event; here, different scenarios based on severity levels of recruitment processes (not a given event) have been taken into account. The three main scenarios proposed are based on previous knowledge of the area and the processes involved. Thus, Scenario 1 was defined as unlikely to occur because the Copyright © 2012 John Wiley & Sons, Ltd.

probability of a landslide occurring in all the delineated areas is very low (defined by different susceptibility levels). However, this is a high-risk flood-prone region (Díez-Herrero, 2003), and therefore, Scenario 3 is the most realistic. Analysing the three cases also allowed us to compare the potential behaviour of all the basins (recruitment capacity) and highlight those areas where recruitment processes may occur. Lienkaemper and Swanson (1987) showed that some morphometric characteristics may be related to wood delivery (e.g. basin size). According to Montgomery et al. (2003), the recruitment processes may have different patterns based on different reaches of a river, with landslides as the predominant process in small steep headwater basins and bank erosion the most important process leading to wood inputs to larger floodplain rivers. The work carried out by Seo et al. (2012) revealed that in small watersheds with narrow channels, mass movements (landslides and debris flows) were major factors in the LWD production and transport; in intermediate and large watersheds with wider channels, flooding was the main factor triggering LWD movement. In the study area, the basin size is not such a significant factor (p-value = 0.36) because the wood recruitment is controlled by the processes that deliver wood to rivers, as shown by the correspondence analysis. The results of the analyses show clearly the different behaviour of some basins in terms of the number of potentially recruitable trees, basin area, forested area and recruitment source areas. These basins (highlighted in bold in Table IV) may have a higher wood recruitment capacity based on the results of the analyses and field observations. Although the sources and mechanism of wood recruitment may vary across regions, the method presented here clarifies and explains these differences on a regional scale, enabling areas for detailed studies or field surveys to be identified. The fieldwork highlighted the need to establish indicators to interpret the LWD dynamics at the reach scale, especially when no direct evidence is found and woody debris has been removed from the river or transported longer distances. The dendrogeomorphic evidence observed in living trees is particularly useful, and a key table to infer the LWD dynamics has been provided. As far as the authors are aware, there are no previously published indicators or tables of this type in the literature. Even so, some cases may not be perfectly correlated. Potential errors and limitations We acknowledge some limitations of the conceptual model, and probably one of the most striking findings is the validation of volume estimations. Although exact numbers are obtained, this analysis may be accompanied by some uncertainties, and the results must therefore be considered as orders of magnitude. One source of uncertainty is the input data used [the level of detail of the procedure is restricted to the detail defined (scale) of hazard, geological, topographical River Res. Applic. 30: 81–97 (2014) DOI: 10.1002/rra


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Table IV. Summary of field observations in the selected basins: YES (with shading), observed; NO, not observed. In bold: groups 1 and 2 for Scenarios 1 and 2, and group 2 for Scenario 3. *See comments.

and forestry maps]. Possible inaccuracies in earlier estimated landslide and flood susceptibility are outside the scope of this work. Besides the previously published landslide and flood maps, we defined stream bank erosion-prone areas. The factors controlling stream bank erosion are complex and interrelated and may require detailed field data. However, the aim here was to analyse this process as a potential wood recruiting mechanism; the stream bank erosion susceptibility was inferred from two other indicators, the stream sinuosity and transport capacity, and the erosion-prone areas were defined following the method used for flooding. The use of different scales and various information sources may also alter the results to some extent. It is not possible to test the theoretical predictions made in this paper fully, but Copyright © 2012 John Wiley & Sons, Ltd.

to estimate the uncertainty related to the delineation of source areas, a sensitivity test of this parameter over the final recruitable tree results was carried out. A 10% interval reduction in the recruitment area was computed, and recruitable trees were recalculated. Figure 12 shows the case for the landslide-prone area. The total wood volume showed a linear decrease ranging from 10% to 35%. Therefore, up to 25% error is associated with the source area delineation. In the delineated source areas, the probability of a tree entering the stream was quantified based on the paper by Robison and Beschta (1990). These probabilities were incorporated into the method using the volume correction factor. This factor therefore reduces the total volume of potential recruitable wood in those areas where the process susceptibility River Res. Applic. 30: 81–97 (2014) DOI: 10.1002/rra

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Figure 12. Model sensitivity analysis. Graph shows landslide-prone

area reduction (%) and recruitable trees reduction (%).

is lowest and/or vegetation resistance is highest. The vegetation resistance was established based on the tree species and forest stage, and on previous work by Hutte (1968), Stumbles (1968), Naka (1982), Abernethy and Rutherford (2001) and Webb and Erskine (2003). This concept may be equivalent to the structural classification of forested areas made by Blaschke et al. (2004) and the woody debris availability indicator used by Mazzorana et al. (in press). The volume correction factor can also be subjective, and the modification of this coefficient may change the final results. However, in the authors’ opinion, 100% recruitment is not reliable; therefore, the maximum probability value (%) was used to obtain maximum potential recruitable volumes.

Potential applications Analysis of the results focused on the basins crossed by rivers provided an overview of those areas potentially at risk from LWD transport during floods. According to our findings, 7 of the 14 basins studied showed enough evidence in the field and in the theoretical calculations to affirm that LWD transport takes place and may be a potential hazard during flood events. Many studies show that woody material transported in the flow is responsible for increasing flood hazard impact (Comiti et al, 2008; Mao et al., 2010; Rickenmann and Koschni, 2010). Hence, woody material transport cannot be ignored in reliable hazard assessment. The authors of this paper also assume that the woody debris contribution is due to an episodic process, a landslide or flood (Wohl et al., 2011), so that transport will be congested (Braudrick et al., 1997). This type of transport may result in clogging or obstructions at critical stream configurations such as bridges. Some studies have included this phenomenon in flood hazard analysis (Mazzorana et al., 2011, in press; Merten et al., 2010; Ruiz-Villanueva et al., 2012, under review). In all these studies, estimates of wood volumes are required as input. Copyright © 2012 John Wiley & Sons, Ltd.

The method presented here may be useful as a first step in the identification of those basins where there may be significant amounts of LWD and in the preliminary definition of wood loads for physically based LWD modelling (Ruiz-Villanueva et al., under review). The proposed methodology can also be used for river and forest restoration and management (Hilderbrand et al., 1998). Knowing the spatial patterns of LWD recruitment can provide a watershed context for understanding geomorphic and ecological processes associated with LWD (Martin and Benda, 2001). This may help land managers to identify the relative importance of different recruitment processes, knowing where and how much LWD is recruited. Forecasts of future conditions could also be simulated using different forestry cover. This allows estimates of changing conditions of source areas and wood volumes in a changing perspective of land use or stand dynamics (Swanson et al., 1998). Scenarios for climate change could also be incorporated. The same is true for predictions based on changing recruitment processes (i.e. types, frequency and severity), so that the recruitment capacity can be analysed at a basin scale.

CONCLUSIONS This paper presents a method to define areas that may contribute to the delivery of woody material to streams on a regional scale. The method takes into account the importance of different recruitment processes (landslides, fluvial transport and bank erosion during floods), creating reliable scenarios based on process severity and providing estimates of recruitable wood volumes for each scenario. The method provides a suitable approach to set realistic basin-wide targets defining the LWD recruitment capacity and estimate the order of magnitude of the maximum wood volume contributed. In the study area, 7 of the 14 basins showed enough evidence in the field and in theoretical calculations to conclude that LWD transport takes place and it could be a potential risk with the highest potential recruitable wood volume (expressed as thousands of trees). The results can be used to characterize the dynamics of woody debris from the perspective of potential risk from its transport during floods. To improve this hazard analysis, it is crucial to predict the relative propensity of streams for the entrainment and delivery of woody material. ACKNOWLEDGEMENTS

This work was funded by MAS Dendro-Avenidas project and the Geological Survey of Spain (IGME). The first author is most grateful to Ángel Prieto and Margarita Sanabria (IGME) for their suggestions in the GIS analysis, and the Spanish Ministry of the Environment for providing the forest map and inventory data. River Res. Applic. 30: 81–97 (2014) DOI: 10.1002/rra


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APPENDIX A. FUZZY ASSOCIATIVE MATRICES.

