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Natural Landslides Which Impact Current Regulating Services: Environmental Preconditions and Modeling

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Recurrent landslide activity in the natural mountain forest is assumed to be a major factor for maintaining its high biodiversity. It is hypothesized that abiotic–biotic interactions are a prerequisite for natural landslides. A statistical model solely driven by topographic predictors can explain areas prone to landslides but also shows that other factors (e.g., geology, soil, climate, vegetation) than topography might play an important role to improve model performance. Thus, the chapter also shows approaches to derive spatial information on soil properties and wind stress as potential driving predictors for the model. Furthermore, it can be shown that even changes in the biogeochemical cycle and the regulation between nutrient input and biomass production might influence the risk of landslides.
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Chapter 12
Natural Landslides Which Impact Current
Regulating Services: Environmental
Preconditions and Modeling
Jo¨rg Bendix, Claudia Dislich, Andreas Huth, Bernd Huwe, Mareike Ließ,
Boris Schro¨der, Boris Thies, Peter Vorpahl, Julia Wagemann,
and Wolfgang Wilcke
12.1 Introduction
Manifold interactions between the abiotic and the biotic environment doubtlessly
exist in the complex biodiversity hotspot of the Rio San Francisco valley. Hitherto,
it is not unveiled how the natural forest and its biodiversity which regulates
(regulating services) the local abiotic conditions (climate, water, soil) is subjected
to feedbacks regarding the preservation of species richness. Different hypotheses
how interactions and feedbacks between abiotic factors and biota contribute to
determine biodiversity are under discussion since decades. Widely accepted in the
J. Bendix (*) • B. Thies • J. Wagemann
Faculty of Geography, Laboratory for Climatology and Remote Sensing, University of
Marburg, Deutschhausstraße 10, 35032 Marburg, Germany
e-mail: bendix@staff.uni-marburg.de
C. Dislich • A. Huth
Department of Ecological Modelling, Helmholtz Centre for Environmental Research – UFZ,
P.O. Box 500 136, 04301 Leipzig, Germany
B. Huwe • M. Ließ
Department of Soil Physics, University of Bayreuth, Universita
¨tsstraße 30, 95447 Bayreuth,
Germany
B. Schro
¨der
Department of Ecology and Ecosystem Management, Technical University of Munich,
Emil-Ramann-Str 6, 85354 Freising-Weihenstephan, Germany
P. Vorpahl
Department of Ecology and Ecosystem Management, Technical University of Munich,
Emil-Ramann-Str 6, 85354 Freising-Weihenstephan, Germany
Institute of Earth and Environmental Science, University of Potsdam, Karl-Liebknecht-Str.
24-25, 14476 Potsdam-Golm, Germany
W. Wilcke
Geographic Institute of the University of Bern (GIUB), University of Bern, Hallerstrasse 12,
3012 Bern, Switzerland
J. Bendix et al. (eds.), Ecosystem Services, Biodiversity and Environmental Change
in a Tropical Mountain Ecosystem of South Ecuador, Ecological Studies 221,
DOI 10.1007/978-3-642-38137-9_12, ©Springer-Verlag Berlin Heidelberg 2013
153
group of hypotheses regarding internal feedbacks controlling biodiversity is the
intermediate disturbance hypothesis (IDH) (Connell 1978; Molino and Sabatier
2001) which means that moderate disturbances are fostering the highest degree of
species richness. Roxburgh et al. (2004) stressed that the original specification of
the IDH requires patchy disturbances. Sheil and Burslem (2003) emphasized that
landslides are proven to be one important patchy disturbance type promoting
biodiversity above and below ground. It is meanwhile undisputed that landslides
are a major factor of natural disturbance in the mountain forest of the study area
(Wilcke et al. 2003, Chap. 1). While reducing the overall aboveground biomass,
landslides increase the spatial heterogeneity of biomass distribution and thus create
distinct habitat types (Dislich and Huth 2012). Particularly plant succession after a
landslide and the related above ground species pool of mosses, lichens, vascular
plants like orchids and pioneer tree species contribute to the high biodiversity of the
mountain rain forest and its resilience against natural disturbances (refer to Chap.
8). Below ground diversity and abundance (e.g., AM fungi) might be affected by
landslides, too (refer to Chap. 7).
Profound knowledge on physical interactions between abiotic factors and the
forest that are assumed to trigger landslides is mandatory for predicting landslide
occurrence probabilities and potential future changes.
The basic factors controlling landslide occurrence in the study area are geology
in terms of bedrock material, climate, and topography (Fig. 12.1).
While the geological substrate in the study area is nearly homogenous, the
topographic situation is highly variable. In this context, elevation, slope position,
steepness, and terrain curvature are the most important factors (Sect. 12.2.1,
Fig. 12.1a). Regarding climatic parameters, particularly the abundant rainfall
enhances the weight of vegetation and soil and reduces soil strength. Because
rainfall generally increases with terrain altitude (Chaps. 1,19, and 24) elevation
is a good proxy for rainfall. High wind speed and resulting dynamic pressure
particularly at windward sides at higher altitudes transfer the dynamic stress of
trees into the tree root layer and thus are also expected to be important predictors to
assess landslide risks (Sect. 12.3.3, Fig. 12.1c). Soil conditions (thickness of the
organic and mineral soil layers, soil water logging conditions as indicated by
stagnic horizon occurrence probability) are suspected to play a major role and
should be considered for landslide prediction (Fig. 12.1b), too. Beyond physical
interactions, also chemical interactions might influence the risk for landslides. The
role of a specific abiotic–biotic interaction — the relation between soil nutrient
availability and fine litter production as a proxy for biomass production and thus
vegetation and organic layer weight (Fig. 12.1d) — is discussed in this chapter.
Nutrient availability in the soil as an important control of biomass production
(influencing the weight of the vegetation) and organic matter degradation
(influencing the weight of organic layers) is thus assumed to be an important
predictor for landslide probability.
To disentangle the processes responsible for landslide activity, spatial explicit
models as presented in this chapter are necessary, which are currently based solely
on topographical predictor variables (Sect. 12.2.1, Fig. 12.1a). For a future
improvement of the presented model, further spatial input data of relevant climatic
154 J. Bendix et al.
and soil predictors as described above are required (Sects. 12.2.212.2.4). Most of
these data were not available when developing the model described in Sect. 12.2.1.
Consequently, this chapter is also devoted to exemplarily present methods to
regionalize point-based soil and climate data.
It should be stressed that landslides in a protected, unused pristine mountain forest
are not a direct ecosystem service (refer to Chap. 4). However, natural landslide
dynamics cause feedbacks to other abiotic and biotic ecosystem components which
give reason to expect impacts on several service levels as, e.g., regulation services.
On the landscape scale, naturally and anthropogenically induced landslides seem to
play a major role in sediment regulation of the catchment, being claimed to be
responsible for a quasi-continuous export of sediment loads independent on precipi-
tation peaks (refer to Chap. 9). On the smaller scale, nutrient regulation is clearly
affected by landslides. Nutrients are removed with the biomass and the organic layers
from the slide area but deposited and concentrated in its foot area (refer to Chap. 11).
Regarding carbon regulation, landslides are characterized by reduced tree growth on
the slides due to the poor nutrient conditions, thus diminishing aboveground carbon
stocks considerably (refer to Chap. 24).
12.2 Methods
12.2.1 The Statistical Landslide Model
Conditions leading to slope failure in the past are likely to cause landslides in the
future as well. Thus, inventories of past landslides combined with topographic
information and thematic maps of controlling factors are used to train statistical
Fig. 12.1 Overview on factors controlling landslide susceptibility in the study area. Arrows
indicate aspects covered by this chapter: The topographic control on landsliding (a), on soil
formation (b), on the distribution of local wind fields (c), and the dependence of organic matter
decomposition, organic layer mass, and biomass dynamics (d). Future model parameters written in
gray are developed and discussed in this chapter
12 Natural Landslides Which Impact Current Regulating Services... 155
landslide models with multiple predictors. Univariate response curves of these
models can provide insights into driving factors of landslides if the following
preconditions are met: (1) The model quality (in terms of performance and calibra-
tion) is sufficient. (2) Consistency between mechanistic assumptions and training
data is maintained. (3) The chosen predictors are interpretable.
(1) Vorpahl et al. (2012) provided a unified framework to train, test and compare
different statistical methods. Applying this framework to eight different
methods from statistics and machine learning (i.e., generalized linear and
additive models, multivariate adaptive regression splines, artificial neural
networks, classification tree analysis, random forests, boosted regression
trees, and the maximum entropy method), they generated weighted model
ensembles.
(2) Vorpahl et al. (2012) maintained consistency between training data and mech-
anistic assumptions by using a subset of five historical landslide inventories of
the RBSF provided by Stoyan (2000) and confined their analysis to landslides
that occurred in an area free of anthropogenic interference (Fig. 12.2). Further-
more, they distinguished different functional units of landslides: i.e., initiation,
transport, and deposition zones. This distinction is of key importance for an
interpretation of univariate model response curves, since linkages between
model predictors and actual mechanisms in the distinct functional units differ.
(3) In a case study, Vorpahl et al. (2012) exclusively used terrain attributes derived
from a digital elevation model (DEM) as predictors: elevation above sea level
(ALT), slope angle, topographic wetness index (TWI), stream power index
(SPI), convergence index (CI), topographic position index (TPI) with two
different radii (100 m and 500 m), and the aspect. To model landslide initiation
as a phenomenon of abiotic–biotic interactions by assessing the importance of
abiotic and biotic predictor values in later applications of the method, spatial
parameter values as presented in the succeeding sections might be helpful.
12.2.2 Potential Model Parameter: Regionalization of Soil
Data
The spatially explicit prediction of histic and stagnic soil horizons is necessary as a
major precondition to understand the landslide dynamics in the study area.
Soil regionalization is based on the general concept (e.g., Jenny 1941) that soil
genesis and, hence, the soils’ distribution throughout the landscape mainly depend
on topography, among other parameters. Therefore, topographic parameters can be
used as predictors to develop digital maps of various soil attributes.
Soil horizons were assessed by 56 soil profiles and 315 auger sampling points.
Key topographical parameters were calculated based on the DEM and implemented
area wide as predictors in the software SAGA GIS. To collect a representative
dataset, sampling sites were selected according to a 24 terrain classes comprising
156 J. Bendix et al.
sampling design along transects extending along side valley slopes (Liess
et al. 2009).
The regionalization as presented here is based on earlier attempts to predict these
horizons (Ließ 2011). In comparison to Ließ (2011), an improvement could be
achieved by focusing on (1) additional terrain parameter selection and by (2)
investigating the dependence on scale as well as (3) the performance of another
recursive partitioning method, Random Forest (RF) (Breiman 2001).
Fig. 12.2 (a) Landslide inventories created by evaluation of aerial photographs of five different
years (i.e., 1692, 1969, 1976, 1989, and 1998) by Brenning (2005) and landslide susceptibility
maps as produced by weighted model ensembles for (b) landslide initiation, (c) transport, and (d)
deposition zones (cf. Vorpahl et al. 2012)
12 Natural Landslides Which Impact Current Regulating Services... 157
(1) Based on the investigations by Ließ (2011), predictors representing climate
(altitude, PISR), water accumulation (curvature, convergence, KRAarea),
water discharge (slope, KRAslope), the insulating effect of the heterogeneous
geomorphology with the ridge—side valley structure in particular (TRI,
normalized height, valley depth) and the wind effect (wind effect, aspect)
were selected to model the soil pattern of this area, all calculated by using
SAGA GIS (Table 12.1,Bo
¨hner et al. 2006).
(2) According to the assumptions of Ließ (2011) that the influence of certain
predictors on soil property development is scale dependent, Brown et al.
(2004) had reported this for the influence of curvature on soil texture, terrain
parameters were calculated for three different GIS raster grid cell sizes (10, 20,
30 m).
(3) Because RF shows a strong dependence on the used dataset used for model
development (Ließ et al. 2012), i.e., the terrain parameters used as predictors
with the soil parameter as response variable, 100-fold RF calculations of the
spatial water stagnation pattern as well as organic layer and stagnic horizon
thickness were carried out. For each of the 100 model runs, the used dataset was
varied by using 9/10 random Jackknife partitions data subsets of the complete
dataset. The 100 models’ prediction results were then averaged and displayed
as two maps: the mean prediction value of the particular soil parameter and its
prediction uncertainty which is represented by the coefficient of variation.
Cross validation is applied to the remaining 1/10 of the dataset, which was
not used to develop the RF models, for model evaluation.
Table 12.1 SAGA modules to calculate terrain parameters
Terrain parameter Module library Module
Altitude Terrain analysis—preprocessing Fill sinks (Planchon/Darboux, 2001)
Slope Terrain analysis—morphometry Slope, aspect, curvature
Aspect
Profile curvature
Plan curvature
Convergence index Terrain analysis—morphometry Convergence Index (search radius)
Normalized height Terrain analysis—morphometry Relative heights and slope positions
Valley depth
TRI Terrain analysis—morphometry Terrain Ruggedness Index
Wind effect Terrain analysis—morphometry Wind effect
KRAarea Terrain analysis—hydrology Catchment area (flow tracing)
KRAslope
SWI Terrain analysis—hydrology Saga Wetness Index
PISR Terrain analysis—lighting, visibility Potential incoming solar radiation—
direct insolation
158 J. Bendix et al.
12.2.3 Potential Model Parameter: Regionalization of Wind
Data
Regionalization of meteorological point observations facilitates the analysis of
interactions between the abiotic environment and biosphere (e.g., Fries et al.
2009,2012). Strong wind pressure to forest trees might be one reason fostering
landslides and shaping the tree line. Therefore, digital wind speed and dynamic
pressure maps are determined using the following procedure: (1) Statistical analysis
of wind speed observations using the Weibull density function. (2) Calculation of
digital wind speed maps by applying a sheltering factor—algorithm to a DEM. (3)
Validation of calculated wind speed using model-independent meteorological
stations. (4) Calculation of dynamic pressure maps based on the tropical standard
atmosphere and the generated wind speed maps.
(1) Point measurements of hourly wind speed data and the wind direction at 2 m
above surface level for a period of 8 years (1999–2006) for five meteorological
stations (Cerro, ECSF, El Trio, Paramo, TS1, Fig. 12.3) were analyzed regard-
ing mean and maximum wind speed. According to meteorological conventions
(e.g., Weisser 2003), mean and maximum wind speed per 45-wind direction
class are derived from the Weibull density function (50 % and 95 % percentile),
where the parameters of the distribution are estimated by the maximum likeli-
hood method.
(2) Wind speed maps are calculated in three steps: First, data of the station Zamora
and the highest meteorological station (Paramo) are used to calculate a linear
decrease of average and maximum background wind speed with decreasing
terrain altitude. Second, the approach of Winstral and Marks (2002) is used to
derive the maximum upwind slope parameter which is a measure of topo-
graphic shelter or exposure relative to a particular wind direction. The finally
determined shelter factor is multiplied with the background wind speed for
every pixel, providing the digital wind speed maps for every wind direction
class.
(3) Wind speed is extracted for the grid points of the meteorological stations not
used for the regionalization and compared to the modeled data. For the most
stations (e.g., ECSF, Cerro), the correlation is significant and well-suited,
except for the station El Tiro which is known to be strongly influenced by
topographic venturi effects not considered by the regionalization method.
(4) Finally maps of average and maximum dynamic pressure are calculated from
the wind speed maps and average air density where the latter is derived by
blending the tropical standard atmosphere with the DEM.
12 Natural Landslides Which Impact Current Regulating Services... 159
12.2.4 Soil Properties and Litterfall
Between 1998 and 2010 we collected data from 12 sites in the study area (one in
each of the microcatchments (MC) 1 and 3, three in each of MCs 3 and 5 and the
four control sites of Nutrient Manipulation Experiment (NUMEX, for locations see
Chap. 1, NUMEX is explained in Chap. 23). Monitoring in MC2 lasted for 12 years,
in MCs 1, 3, and 5 for 5 years and in NUMEX for 1 year. At each site, mass of
organic layer was determined once by measuring depth and densities of the organic
horizons (Oi, Oe, and Oa) and mass of fine litterfall was determined with three- to
sixfold replicated 0.3 0.3 m
2
to 0.6 0.6 m
2
large litter traps in at least monthly
resolution. Furthermore, free-draining litter lysimeters just below the organic layer
were used to collect litter leachate in weekly to fortnightly resolution in which
mineral N (NH
4
+
–N + NO
3
–N) concentrations were determined with a Continu-
ous Flow Analyzer and K, Na, Ca, and Mg concentrations with flame Atomic
Absorption Spectrometry.
12.3 Results and Discussion
12.3.1 Statistical Landslide Modeling
With the exception of classification tree analysis all techniques performed compar-
atively well while being outperformed by weighted model ensembles (refer to
Vorpahl et al. 2012 for details). As expected, models trained on different functional
units of landslides led to different model outcomes (Fig. 12.2).
Fig. 12.3 Univariate response curves (black lines) and predictor importance scores of weighted
ensembles of statistical models. Response quartile ranges are shaded in gray. The curves in each
column show the probability of observing a landslide initiation, transport or deposition zone as a
function of a single predictor variable, i.e., elevation above sea level (ALT), the convergence index
(CI), indicating small scale concavities (CI <0) or convexities (CI >0), the topographic wetness
index (TWI), the slope angle (Slope), the stream power index (SPI), the topographic position index
(TPI), describing the difference between local elevation and the mean elevation within two
different radii of 100 m (TPI.100) and 500 m (TPI.500), respectively, and the direction of the
steepest slope angle (Aspect) (Vorpahl et al. 2012)
160 J. Bendix et al.
Univariate model response curves to changes in predictor values—also called
partial dependency plots (Fig. 12.3)—show that landslide deposition zones tend to
be located at valley bottoms, indicated by high values of SPI and TWI as well as by
negative values of CI and TPI.
Landslides follow the local topography by sliding along shallow ducts in the
slope as indicated by a maximum susceptibility for transport zones at slightly
negative values of CI and TPI. The response curve for initiation zones to changes
in slope indicated an increasing contribution up to ~52. Even steeper slopes lead to
a decrease of landslide susceptibility. This can be attributed to the fact that on
extremely steep slopes the soil layer is usually thinner and hence insufficient for
landslide initiation.
Model response to elevation above sea level exposed an increasing landslide
initiation probability with elevation up to 2,400 m a.s.l. At higher elevations,
landslide initiation probability decreases. Rollenbeck (2006) reported an altitudinal
increase of average precipitation (from about 2,050 mm a
1
at 1,960 m a.s.l. up to
4,400 mm a
1
at 3,200 m a.s.l.) in the research area. If rainfall is an important
factor, this should hint towards a positive correlation between precipitation and
landslide susceptibility which contradicts the above finding. As additional factors at
the intersection from dense forest into the Pa
´ramo, lower standing biomass and
lower inclination may strongly reduce landslide formation. Furthermore, Bussmann
et al. (2008) gave a possible explanation for the decreasing landslide susceptibility
at higher elevations by a change in soil substrate from slightly metamorphosed
clayey/sandy sediments, originating from phyllites, at the lower and intermediate
elevations to a more quartzite rich substrate at higher elevations.
Other altitudinal gradients reported for the research area are related to vegeta-
tion. The decrease of average tree heights with higher elevations (Bra
¨uning et al.
2008), for example, may cause a reduced contribution of plant biomass to slope
instability. Smaller trees are less capable of transferring wind forces into the ground
via a turning moment. Soethe et al. (2006a,b) as well as Leuschner et al. (2007)
reported an altitudinal change in tree root structure and in the ratio of aboveground
to belowground biomass. Thus an increase of root contribution to slope stability at
higher elevations can be additionally suspected.
12.3.2 Digital Soil Maps
To predict organic layer thickness, the models based on 20 or 30 m DEM resolution
performed better than those using 10 m. Regarding the prediction of the occurrence
of a stagnic color pattern, all models using 10 m resolution performed better or
equally well than those of lower resolution. Chaplot et al. (2000) found prediction
accuracy to be highly dependent on DEM resolution: Regarding the prediction of
hydromorphic features 10 m DEM resolution outperformed lower resolutions.
Compared to the median r
xy
resulting from CART methodology and a smaller
number of prediction parameters (Ließ 2011), model performance was now
12 Natural Landslides Which Impact Current Regulating Services... 161
improved for all three predicted soil parameters: regarding stagnic horizon occur-
rence probability it was improved by 0.1 (0.6), regarding horizon thickness it was
doubled (0.36), and regarding organic layer thickness it was more than doubled
(0.47).
The digital soil map of the stagnic horizon occurrence probability is shown in
Fig. 12.4a, b. A low coefficient of variation (10 % for >80 % of the area, see
Fig. 12.4b) shows that the dataset is well suited to model the stagnic properties
pattern within this area. The influence of the relative slope position on the occur-
rence probability is clearly visible: The exposed mountain ridges between 2,100
and 2,650 m a.s.l. display a very high probability of stagnic soil properties, >0.8,
which is decreasing down the side valley slopes to a probability of 0.4 (minimum
¼0.2). The flat platform-like areas on top of the ridges, display a particularly high
probability of >0.9. The areas below 2,100 and above 2,650 m a.s.l. are predicted
with an overall lower probability. Below 2,100 m a.s.l. the lower bulk density (Ließ
et al. 2011) and above 2,650 m a.s.l. the coarser soil texture (Ließ et al. 2012)
leads to a higher saturated hydraulic conductivity and therefore less chance for
the development of stagnic soil properties. For the development of the model to
predict stagnic horizon occurrence probability, all terrain parameters were included.
ab
d
c
ef
Fig. 12.4 Mean stagnic horizon occurrence probability (a) and thickness (c) with coefficient of
variation (b,d) and mean organic layer thickness (e) with coefficient of variation (f) (Overlaid hill
shading with light source from north)
162 J. Bendix et al.
This confirms the assumption that it is the complex pattern of climate (altitude, PISR),
water accumulation (curvature, convergence, KRAarea), water discharge (slope,
KRAslope), the insulating effect of the heterogeneous geomorphology with the
ridge—side valley structure in particular (TRI, normalized height, valley depth) as
well as the wind effect (wind effect, aspect) which lead to the distribution pattern of
stagnic soil properties within the investigation area.
The model to regionalize stagnic horizon thickness is less stable than the model
to predict the horizon’s occurrence probability. This is indicated by the higher
values of the coefficient of variation in Fig. 12.4d. Ließ (2011) describes similar
results. According to Park and Vlek (2002), soil attributes of which the vertical
distribution is strongly determined by pedogenesis or unknown factors are poorly
modeled by environmental variables. Accordingly, the frequent change of parent
material within one soil profile (Ließ et al. 2012) might be the reason why stagnic
horizon thickness cannot be explained by geomorphology alone. The thickest
stagnic layers >40 or even >60 cm are found along the mountain ridges, with
decreasing thickness while proceeding down side valley slopes.
The low uncertainty of the digital soil map of the organic layer (Fig. 12.4e, f)
indicates a stable model. The thickest organic layers are found on mid-slope
positions, decreasing towards the creeks and towards the crests. Furthermore, altitude
is not among the five most influential predictors of organic layer thickness and there is
no correlation between the occurrence of stagnic horizons and organic layer thick-
ness. This is unexpected because in previous work it was shown that the crests had
usually thicker organic layers than the valley bottom positions in the study area
(Wilcke et al. 2010) in line with reports from a similar forest in Puerto Rico (Silver
1994). Furthermore, studies in Costa Rica (Marrs et al. 1988; Grieve et al. 1990)and
at our study site in Ecuador (Schrumpf et al. 2001; Wilcke et al. 2008a,b)haveshown
that organic layer thickness usually increases with increasing altitude because of
decreasing microbial turnover of organic matter with increasing altitude (Benner
et al. 2010). Table 10.1 shows a general trend towards increasing organic layer
thickness with altitude. However, the transect that was investigated covers a much
larger distance (30 km compared to c. 4 km), and spatial data coverage is therefore
limited. Taking a closer look, the results of Chap. 10 also do not describe any positive
correlation between organic layer thickness and altitude for the altitudinal range
between 1,890 and 3,060 m a.s.l. studied here. Finally, it is assumed that soil
waterlogging limits organic matter turnover (Schuur and Matson 2001;Roman
et al. 2010) which results in the expectation of a positive correlation between the
occurrence of waterlogging (as indicated by stagnic horizons) and organic layer
thickness. However, there is a considerable variation in organic layer thickness at
small scale (Wilcke et al. 2002,2008b) illustrating that none of altitude, topographic
position, and waterlogging alone can explain the entire variability in organic layer
thickness.
A possible explanation for the seeming contradictions might be that our dataset
is representative for the whole study area and therefore also includes landslide sites
with incomplete organic layers which form an important part of the studied forest
area (Bussmann et al. 2008). Wilcke et al. (2003) have shown that the full
12 Natural Landslides Which Impact Current Regulating Services... 163
regeneration of the organic layer only occurs at the time scale of a few decades. It
also seems likely that waterlogging favors the initiation of landslides because of the
associated high soil weight (Ließ et al. 2011). The results in the literature, in
contrast, usually refer to undisturbed old-growth forest sites. An alternative expla-
nation might be that litterfall rates are lower on crest sites than at lower topographic
positions associated with a smaller accumulation of organic matter on top of the
mineral soil. However, in Sect. 12.3.4 we show for a limited dataset of 12 study
sites that the decrease in litterfall rates is overcompensated by the decrease in
degradation rates resulting in even higher organic layer thickness at low litterfall
rates. We conclude that the relationships of altitude, topographic position, and
waterlogging with organic layer thickness might have to consider the state of
succession after landslide to explain and predict the spatial distribution of organic
layer thickness in the study area.
12.3.3 Digital Wind Maps
Figure 12.6a shows the calculated digital map of maximum wind speed which
reveals spatial structures comparable to the map of mean wind speed (not shown
here). Maximum wind speed increases with altitude but is locally modified by
topographic shelter effects towards the predominant wind direction. Obviously,
steep and narrow valleys and ravines breaching the Cordillera exhibit the lowest
wind speeds (partly close to calm) on a specific altitudinal level. It is striking that
especially the east-facing slopes without any protection by upstream topographic
structures exhibit severe wind speeds up to 17 m s
1
. The reason is the all-year
dominating circulation from the east (Rollenbeck and Bendix 2011) impinging
particularly the eastern slopes of the Cordillera. The high wind speeds at relatively
low altitudes are a result of the Andean depression (Chap. 1) which allows the
easterlies to affect the upper mountain areas nearly unbridled. By blending the land
use classification of Go
¨ttlicher et al. (2009) with the digital maps of mean and
maximum dynamic pressure, the interaction of wind pressure and trees can be
assessed, e.g., for the tree line ecotone (Fig. 12.5b). The statistical evaluation
clearly reveals that the trees at the treeline of the eastern escarpment exhibit clearly
stronger mechanical exposure than on the western slopes where in the most
situations, wind dynamic pressure falls into the lowest category (mean <5Nm
2
;
maximum <20 N m
2
).
12.3.4 Chemical Interactions: Soil Nutrients and Litter
There were close positive correlations between nutrient concentrations in soil
solution and annual fine litterfall as proxy of biomass productivity and close
negative correlations between nutrient concentrations in soil solution and mass of
164 J. Bendix et al.
a
b
Fig. 12.5 (a) Digital map of maximum wind speed [95 % percentile] determined as an
occurrence-weighted average of eight wind direction classes. (b) Mean and maximum dynamic
pressure depending on aspect along the tree line ecotone
12 Natural Landslides Which Impact Current Regulating Services... 165
organic layer (Fig. 12.6). The effect of bases (K, Na, Ca, and Mg) is more
pronounced than that of N. Consequently, increased nutrient availability resulted
in increased fine litterfall production and—if litterfall is proportional to standing
biomass—increased weight of vegetation. The increased weight of vegetation is
counteracted by decreased weight of organic layer because of a faster turnover at
higher nutrient availability. Expressed in percent of the intercept the response of
litterfall is more pronounced (35 % for N and 139 % for bases) than that of mass of
organic layer (11 and 49 %) suggesting that the standing biomass will more
strongly increase than the mass of the organic layer decrease if nutrient availability
improves. The latter can be expected for the near future because of increased
dryness enhancing release of nutrients from the organic matter by mineralization
and because of rising deposition of reactive nitrogen and possibly also of base
metals because of the shortening of the El Nin
˜o Southern Oscillation (ENSO) cycle
(see Chap. 11). This might imply that the total weight of vegetation plus organic
layer will increase in the near future in response to environmental change thereby
enhancing the risk of landslides.
Nutrient availability in the study area generally decreases with increasing
altitude and at the same altitude is different between valley bottom and ridge top
position (Wilcke et al. 2008b,2010). Furthermore, the frequently occurring shallow
landslides in the study area remove the vegetation and the organic layer resulting in
nutrient loss which is only replenished during a few decades (Wilcke et al. 2003).
Fig. 12.6 Relationship between mean mineral N concentrations (NH
4
+
–N + NO
3
–N) in litter
leachate and (a) mean annual litterfall and (b) mass of the organic layer and between mean sum of
base metal concentrations (charge equivalents of K, Na, Ca, and Mg) in litter leachate and (c) mean
annual litterfall and (d) mass of the organic layer. Mean nutrient concentrations in litter leachate
and mean annual litterfall was determined during 1–10 years depending on the specific site
166 J. Bendix et al.
The latter effect is not included in the relationship between nutrient availability and
litterfall/mass of organic layer in Fig. 12.6 because all our measurements were
taken at old-growth forest sites which were not impacted by landslides in the last
decades. The high variability of altitude and topography in our study area results in
a high spatial variability of nutrient availability and thus also a high ability of
organic layer mass and standing forest biomass together determining the weight on
top of the mineral soil (Wilcke et al. 2002; Moser et al. 2008).
12.4 Conclusion
The presented statistical model ensembles revealed that the occurrence of
landslides is mainly controlled by factors related to the general position along a
slope (i.e., ridge, open slope, or valley). However, there is a clear contradiction
between the altitudinal gradient of rainfall (increasing with altitude) as an assumed
major trigger and landslide probability (decreasing with altitude above 2,400 m a.s.
l.). This indicates that more complex interactions control landslide activity in the
study area which can be explained with a model ensemble purely forced with
DEM-derived proxy predictors. Digital soil maps show a sandier soil texture and
lower soil water logging probability above 2,400 m a.s.l and hence provide a good
explanation. We further assume that variation in above and belowground biomass
mitigating dynamic wind pressure to the forest in the higher parts are major factors
causing these contradictory findings. Thus, it is necessary to provide further spatial
predictor maps related to geology, vegetation biomass, and climate. By additionally
considering predictors related to vegetation, soil and climate, statistical models will
allow for predicting potential future changes in landslide probability patterns.
Dynamic forest models like FORMIND can be used to further quantify effects on
the aboveground biomass production (Chap. 24).
Regarding maps of soil conditions, statistical models based on comprehensive
soil field surveys are applied to spatially predict organic layer and stagnic horizon
thickness as well as stagnic horizon occurrence probability. Forcing parameters are
solely derived from topographical analyses of the DEM. Even if the main influence
of the relative slope position as exposed mountain ridges and flat platform-like
areas on top of the ridges are the best predictors for the occurrence probability of
stagnic horizons, the results point out complex interactions of different factors
behind this. Particularly, the determination of the stagnic horizon thickness is less
stable, most likely due to unconsidered, non geomorphologic factors. For prediction
of organic layer thickness, the degree of succession after landslide might also play
an important role and should be considered besides the well established relationship
of waterlogging, topographic position, and altitude with organic layer thickness.
Digital maps of mean and maximum wind speeds as well as dynamic wind
pressures as additional potential forcing parameters were derived by means of field
observations of wind speed, data on air density, and a DEM by introducing a terrain
shelter factor. It could be shown that dynamic pressure to the forest generally
12 Natural Landslides Which Impact Current Regulating Services... 167
increases with altitude but differs with exposition to the main wind direction.
Because easterly wind directions are predominant, the tree line ecotone on the
eastern slopes is affected by clearly higher wind stress.
Finally, it could be shown that interactions in the biogeochemical cycles might
be relevant for the risk of landslides. Nutrient availability in soil influenced litterfall
production positively and organic layer thickness negatively. An increased nutrient
availability in the future will most likely result in an increase of standing biomass,
thus, enhancing the risk of landslides in response to future environmental change.
Regarding ecosystem services, landslide dynamics will influence different ser-
vice levels. As emphasized in the introduction, landslides are most likely a precon-
dition for the high biological diversity in the mountain forest and thus, directly
related to the cultural services of the forest (Chap. 4). Because the forest structure
characterized by its high species richness properly regulates abiotic processes,
landslides indirectly contribute to the regulating services of the forest (Chap. 4)
too. On the scale of a single landslide, regulation of abiotic parameters changes
significantly. For instance, microclimate (temperature, humidity) regulation is
reduced in comparison to areas sheltered by tree canopies (Fries et al. 2009,
2012; Chap. 9). On this scale also sediment and nutrient regulation are affected.
While sediment and its nutrient is accumulated at the forest edges on the foot of the
slide, also slope wash of matter is higher in young landslides than under natural
forest (e.g., Larsen et al. 1999). On the scale of the forest as a mosaic of trees and
gap areas originating, e.g., from landslides, mass and energy fluxes to the atmo-
sphere are different than those of closed canopies (e.g., Zhang et al. 2007) which
means that landslides maintain the specific regulation of forest–atmosphere
interactions. Also the carbon regulation function of the mountain ecosystem is
determined by the landslide occurrence. Landslides increase carbon turnover and
change the forest composition towards a higher fraction of pioneer species—
however overall forest productivity may be reduced compared to old growth forest
without landslide disturbances due to the unfavourable environmental conditions on
landslide sites (Chap. 24).
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Shallow landslides are an important type of natural ecosystem disturbance in tropical montane forests. Due to landslides, vegetation and often also the upper soil layer are removed, and space for primary succession under altered environmental conditions is created. Little is known about how these altered conditions affect important aspects of forest recovery such as the establishment of new tree biomass and species composition. To address these questions we utilize a process-based forest simulation model and develop potential forest regrowth scenarios. We investigate how changes in different trees species characteristics influence forest recovery on landslide sites. The applied regrowth scenarios are: undisturbed regrowth (all tree species characteristics remain like in the undisturbed forest), reduced tree growth (induced by nutrient limitation), reduced tree establishment (due to thicket-forming vegetation and dispersal limitation) and increased tree mortality (due to post-landslide erosion and increased susceptibility). We then apply these scenarios to an evergreen tropical montane forest in southern Ecuador where landslides constitute a major source of natural disturbance. Our most important findings are