**3.3 Modelling the protective effect at a catchment scale - A first glimpse**

In the next step, after the field research and after designing the method for estimating the volume of accumulated material, our aim was to exploit the results in the regional model at a catchment scale, as simple regional modelling is the very effective way to implement the results into the decision making process. Until now, there have been several models developed for the quantification of erosional rates within small catchments, some of them mentioned earlier in the text. A comprehensive summary of these models is presented by Gyssels et al. (2005), with special attention paid to the role of vegetation in these models. All of these models confirm the increase of soil erosion with the decreasing percentage of vegetation cover.

Gyssels et al. (2005) also show that these models ascribe a protective role to biomass above the surface, but give little attention to the roots. Moreover, many of these models are designed for grasslands and agricultural plants, but the protective role of forests against soil erosion is much higher (Kirkby, 1980). As the monitoring and quantification of fallen trees in forest ecosystems is difficult, the traditional models incorporate variables related to living (standing) trees, i.e., to forest canopy structure. Therefore, we had to establish a new model to simulate the regional protective effects of trunk dams.

We used the GIS-based distributed (GRID-based) model that was applied to the experimental catchment, and the input values were set according to the results of the field survey. The first results of modelling were based on a simplified VAM equation implemented in the GIS environment (ESRI ArcView 3.2, ArcGIS 9.2).

Biogeomorphologic Approaches to a Study of Hillslope Processes Using Non-Destructive Methods 33

32 Studies on Environmental and Applied Geomorphology

• the internal structure, stratification and compaction of the accumulated material (although this can also reflect initial processes that are responsible for the accumulation

• the age, distribution and position of the wood fungi, which is to be discussed in section

Based on the field survey, Raska & Orsulak (2009) proposed a hypothetical model of the three-way development of a trunk dam in the mid-segment of the hillslope. The three ways are the following: (i) stabilisation of the trunk dam by vegetation cover, (ii) dam breach and formation of a new trunk dam and (iii) denudation after the dam breach without the

In the next step, after the field research and after designing the method for estimating the volume of accumulated material, our aim was to exploit the results in the regional model at a catchment scale, as simple regional modelling is the very effective way to implement the results into the decision making process. Until now, there have been several models developed for the quantification of erosional rates within small catchments, some of them mentioned earlier in the text. A comprehensive summary of these models is presented by Gyssels et al. (2005), with special attention paid to the role of vegetation in these models. All of these models confirm the increase of soil erosion with the decreasing percentage of

Gyssels et al. (2005) also show that these models ascribe a protective role to biomass above the surface, but give little attention to the roots. Moreover, many of these models are designed for grasslands and agricultural plants, but the protective role of forests against soil erosion is much higher (Kirkby, 1980). As the monitoring and quantification of fallen trees in forest ecosystems is difficult, the traditional models incorporate variables related to living (standing) trees, i.e., to forest canopy structure. Therefore, we had to establish a new model

We used the GIS-based distributed (GRID-based) model that was applied to the experimental catchment, and the input values were set according to the results of the field survey. The first results of modelling were based on a simplified VAM equation

centre soft and (iv) totally rotten (e.g., Collective, 2007);

• the stage of plant succession on the top of the trunk dam body;

**3.3 Modelling the protective effect at a catchment scale - A first glimpse** 

to simulate the regional protective effects of trunk dams.

implemented in the GIS environment (ESRI ArcView 3.2, ArcGIS 9.2).

of material);

formation of a new trunk dam.

4.

vegetation cover.

experiment to assess the decomposition process of four species (*Pseudotsuga*, *Tsuga*, *Abies* and *Thuja*) and concluded that the decomposition is significantly influenced by the initial wood chemistry and by the colonisation pattern, especially the penetration of the bark barrier and colonisation by wood fungi. Harmon et al. (2000) have presented a new method for estimating biomass loss by wood decay. Their results have shown that in most cases, the biomass loss has negative exponential progress, while in one case (*Pinus sylvestris*), the regression trend was polynomial, displaying different phases of decomposition. The relative dating of trunk decomposition is usually made by distinguishing categories of (i) hard timber, (ii) edge soft - centre hard, (iii) edge hard -

Fig. 7. Model of the protective effect of trunk dams within the experimental catchment. A slope inclination derived from DEM (1:10000); B - land cover classes CORINE 2006; C hypothetic calculated volume of accumulated material; D - calculated volume of accumulated material weighted by slope inclination (pixel size 20x20 m). Data sources: CORINE 2006 (European Environmental Agency), digital elevation data (CUZK).

The cross-section of a body of a trunk dam was assumed to be in contact with a trunk and having the shape of a rectangular triangle (Fig. 5B). Thus, the only necessary values for the calculation of VAM were the accumulation width "a", the length of trunk "L" and the slope inclination "α". Instead of trunk radius "r", we set the average "x" value (Fig. 5B) calculated from the field survey results. The variability of this value depends on the forest structure and age within the modelled catchment and on the surface roughness influencing height of a trunk above the surface. In the present study, the structure and age of forests is relatively uniform (Fig. 7B). There are no significant differences in trunk radius variations among the studied sites, and therefore, we set the uniform "x" value (0.35 m). Similarly, the length of trunks was set as an average from the field survey, which was 2500 m.ha-1. Thus, the results of the simplified VAM model will correspond only to the expression of slope inclination. The slope inclination was derived from a digital elevation model (DEM; Fig. 7A). The pixel

Biogeomorphologic Approaches to

discussed by Kauserud et al. (2008).

dynamics and their usual habitats.

• colonisation rates on downed wood; • maximal density of individuals;

1961), but at smaller time scales;

Specie Habitat *Climacocystis borealis* (Fr.) Kotl. & Pouzar conifers *Trichaptum abietinum* (Dicks.) Ryvarden conifers

a Study of Hillslope Processes Using Non-Destructive Methods 35

Polyporous fungi belong to dead-wood-dependent organisms, and they contribute to the continuity of forest renewal by accelerating wood decay processes. The fungi-accelerated wood decay, in turn, improves the integrity of forest ecology and, therefore, the ecology of polypores must be reflected in forest management approaches (Lindblad, 1998). The diversity and abundance of polyporous fungi depends on the species diversity of trees, forest connectivity and the number of downed logs (Edman & Jonsson, 2001; Hottola, et al. 2009). Species richness also plays a role in the decay stage of wood and tree diameter, although the latter relation is a current topic of discussion (Junninen & Komonen, 2011). Although some polypores are able to establish ectomycorrhizal relationships, most polypore species are wood-dependent parasites, saprotrophs and necrotrophs. The reproduction principles and their relation to life histories and the diversity of polypore species are well

*Fomitopsis pinicola* (Sw.) P. Karst. both conifers and broad-leaved trees *Chondrostereum purpureum* (Pers.) Pouzar both conifers and broad-leaved trees

*Daedalea quercina* (L.) Pers. broad-leaved trees (especially *Quercus*)

Table 3. Selected Central European polypore species suitable for the indication of log jam

ecological and morphological characteristics of polypores, especially the following:

• their position on downed wood (horizontal growth of individuals, groups);

The fundamental importance for biogeomorphologic studies is represented by the basic

• relation of polypores to concrete tree species (relation to conifers or broad-leaved trees -

• age of individuals - the age of a log jam may be deduced from the largest individual similar to the techniques used in the lichenometric dating of rock surfaces (Beschel,

*Ganoderma applanatum* (Pers.) Pat. mostly broad-leaved trees

*Daedaleopsis confragosa* (Bolton) J. Schröt. broad-leaved trees *Fomes fomentarius* (L.) J. Kickx f. broad-leaved trees *Phellinus igniarius* (L.) Quél. broad-leaved trees *Plicaturopsis crispa* (Pers.) D. A. Reid broad-leaved trees *Trametes hirsuta* (Wulfen) Pilát broad-leaved trees

see Table 3 for overview of selected polypore species);

• growth rates (annual and perennial, visibility of increments); • morpholgy of individuals and their deformations (tilting, rotation).

size of distributed model was set to 400 m2, which reflects the precision of the input digital data (cf. Hengl, 2006). As the accumulation width is calculated from slope inclination and the "x" value, it can reach high values in locations with flat terrain; but, in fact, the accumulation width is always limited by the neighbouring trunk dams. The maximum accumulation width can be derived from a simple equation assuming the regular distribution of trunk dams:

$$\mathbf{a} \cdot \mathbf{max} \,\mathbf{a} = \mathbf{PS}^2/\mathbf{t} \,\mathbf{L} \tag{2}$$

where PS is pixel size (i.e., 20 m for a pixel area of 400 m2), and tL is the total length of trunks per pixel (i.e., 100 m). The result is a maximum accumulation width of 4 m, which was the value put as an upper limit into the model before acquiring the results of VAM.

The results of the modelling are shown in Fig. 7C, indicating that VAM varies between 1 - 70 m3 per 400 m2, i.e., 25 – 1750 m3.ha-1, but these values seem to be far from the real situation. The reason is that the "x" value was set constant, and therefore, the model assumes that the lower the slope inclination, the higher the accumulation width (see above) and VAM (see also Fig. 6C). In fact, the real processes operating on low gradient slopes will hardly enable accumulation of the material in the trunk dam with a similar efficiency as on highly inclined slopes. Evidently, there are other factors that influence the potential of trunk dams to be filled with accumulated material, such as the length of slope above the trunk dam, the surface material on the slope, and the disturbance regimes affecting the movement of material (e.g., overland flow, zoodisturbances, forest management measures). However, the slope inclination tends to be the most important of these factors according to our observations. The model (Fig. 7C) was therefore weighted again by the slope inclination to give higher importance to slopes with a high gradient and vice versa. The results of VAM in Fig. 7D vary between 2.2 - 4.0 m3 per 400 m2, which corresponds quite well to the empirical values gained during the field survey. Nevertheless, the development of the model is still in progress, and the results may differ when computed for more variable input values.
