PIAHSProceedings of the International Association of Hydrological SciencesPIAHSProc. IAHS2199-899XCopernicus GmbHGöttingen, Germany10.5194/piahs-371-181-2015Monitoring and modeling slope dynamics in an Alpine watershed – a
combined approach of soil science, remote sensing and geomorphologyNeugirgF.f.neugirg@ku.deKaiserA.andreas.kaiser@tbt.tu-freiberg.deSchindewolfM.BechtM.SchmidtJ.HaasF.Department of Physical Geography, Catholic University of Eichstätt-Ingolstadt, Eichstätt, GermanySoil and Water Conservation Unit, Technical University Bergakademie Freiberg, Freiberg, GermanyF. Neugirg (f.neugirg@ku.de), A. Kaiser (andreas.kaiser@tbt.tu-freiberg.de)12June201537137118118717March201517March2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://piahs.copernicus.org/articles/371/181/2015/piahs-371-181-2015.htmlThe full text article is available as a PDF file from https://piahs.copernicus.org/articles/371/181/2015/piahs-371-181-2015.pdf
Steep and unvegetated slopes in mountainous areas play an important role in
erosion research as they deliver large quantities of sediments to the
lowlands. However, their complex hydrological process combinations are
challenging for any modelling and forecasting intention. Due to its high
morphodynamic activity the Lainbach valley in southern Bavaria, Germany, has
repeatedly been subject to studies on erosional processes. We present a
further developed approach of physically based erosion modelling on strongly
inclined and heavily dissected slopes. Model parameters were spatially and
temporally distributed and a statistical model was tested to compare both
findings to a previous study in the same catchment on a different slope.
High resolution surface models from laser scans served as validation for the
modelling results and for monitoring soil loss. Especially an adjustment of
hydraulic roughness values improved the results, whereas rill hydraulics
demand further investigation for future model development. The study at hand
focusses on the summer period and reveals adequate modelling results
(98.4 % agreement in volume loss) with regard to the slope's
non-stationary behaviour but leaves room for improvement for the winter
period.
Introduction
Fluvial erosion on sparsely or unvegetated hillslopes is the major sediment
source in the Lainbach valley, located in the northern Alps. Several studies
have focussed on acting geomorphological processes at these slopes (Becht,
1986; Kaiser et al., 2014; Neugirg et al., 2014; Wetzel, 1992; Schindewolf
et al., 2015). This study aims to improve modelled sediment yields with
regard to a first attempt on a neighbouring slope and to further develop
both a statistical and a physically based erosion model for their
application in Alpine conditions.
Terrestrial laser scanning (TLS) also referred to as terrestrial LiDAR
(Light Detection And Ranging) has become
a well established tool in geoscientific studies. Especially detection of
surface changes in hydrological studies (Baewert and Morche, 2014; Milan et
al., 2007) or volumetric changes due to mass movements, like debris flows
(Bull et al., 2010; Schürch et al., 2011) and rock falls (Abellán et
al., 2011; Haas et al., 2012b) can be acquired with a drastically increased
spatial resolution compared to previously used geomorphological measurement
tools, like erosion pins (Della Seta et al., 2009) or sediment traps (Haas,
2008). Besides the spatial resolution, the contact-less acquisition of data
is another major advantage of LiDAR. In the present study we repeatedly
produced LiDAR data to measure surface changes and soil losses and compare
these results to our erosion model predictions for the same period.
Soil erosion models improved during the last decades and are helpful in
research as well as on the administrative level. Concurrently with the
increased experimental effort in erosion research (Iserloh et al., 2013;
Wirtz et al., 2013; Castillo et al., 2012) a gain in computing and data
acquisition capacities allowed for higher resolution terrain models produced
either by TLS or by SfM procedures. In this regard, physically based erosion
models might offer a powerful tool for process differentiation of steep
slope dynamics. Although the physically based EROSION 3D soil loss
simulation model has been extensively validated (Starkloff and Stolte,
2014; Jetten et al., 1999, 2003; Defersha et al., 2012),
applications on steep slopes go along with new challenges. The high spatial
heterogeneity related with a limited accessibility of such areas is
hindering parameter identification prior to model parameterization. Since
first parameter identifications succeeded in 2013 including rainfall
simulations (Kaiser et al., 2014) model parameterization for the Lainbach
valley site became possible. Nevertheless, the natural non-stationarity of
the catchment and the different processes during summer and winter were
challenging for the model even though it works event based and is sensitive
for heavy rainfall and discharge. Strong inclinations trigger processes like
rock fall or small-scale mudflows which are not existent on agricultural
land, where the model was developed. All rills on the slope are highly
ephemeral with a quick response to rainfall and long inactivity during dry
conditions which justifies the application of an event based soil loss
model.
As a first application of the physical model presented in Schindewolf et
al. (2015) showed room for improvement in terms of correct localization of
dynamic rill areas and total sums of detachment, the present study
successfully tackled these issues. To test the practicability of
transferring the model to other areas a comparable slope in the same area
with only few distinctions such as exposure and slope length was chosen.
The study area is located in the Lainbach valley
catchment in the Northern Bavarian Alps, Germany.
The Lainbach valley catchment
The Lainbach valley catchment is an alpine mountain catchment in the
northern Alps (Fig. 1), which has its highest point at 1801 m (Benediktenwand).
The outlet of the catchment at ∼ 700 m is
located at the town of Benediktbeuern in Upper Bavaria, about 60 km south of
Munich. Although large areas of the catchment are vegetated with mixed
forests (Becht and Kopp, 1988), several sparsely vegetated or completely
uncovered erosional scars can be found. All of these erosional scars,
according to Becht and Kopp (1988), had their maximum spatial extent on
aerial photographs from 1959. The erosional scars are situated at local
valley fillings that have been the result of several advances of the glacier
Isar-Loisach. Becht (1992) mentions a thickness of the valley fillings of
∼ 150 m for the investigated slopes in this study.
Kaiser et al. (2014) supports the assumptions of Wetzel (1992) and revealed very high
bulk densities for the hillslopes. The monitored slope in this study is
close located to the slope studied in Kaiser et al. (2014),
Neugirg et al. (2014, 2015) and Schindewolf et al. (2015). Both
slopes show nearly the same average slope gradient, the same height above
sea level and the same bulk density of the substrate. Additionally, the
precipitation and temperature conditions are comparable, since both slopes
are located almost next to each other. The aspect of both slopes and the
average slope length is entirely different (Table 1).
Topographical data and relief parameter.
Parameters for the hillslopeParameters for the hillslopepresented in this studypresented in Schindewolfet al. (2015) and Neugirget al. (2014, 2015)height above s.l. in∼ 1000 m∼ 1000 maspect of the slopeEastWestaverage slope length35 m8 maverage slope gradient50∘51∘bulk density of substrate1930 kg m-31930 kg m-3Data acquisition and model description
The results presented in this study are based on two field work campaigns
that were carried out on May 2014 and October 2014. The acquisition dates
have been chosen to represent the summer period in the best way and that all
winterly effects (snow cover during spring, fallen leaves in the rills in
autumn) could be minimized.
Data acquisition using TLS
TLS data were acquired using a Riegl LMS Z420i in combination with an on-top
mounted Nikon D700 DSLR camera. The DSLR camera allows to colourize the
point cloud during post processing for better orientation and filtering
procedures. Two (May 2014), respectively three (October 2014) scan positions
were used to minimize shadowing effects, due to heavily incised rills and
gullies on the slope. The alignment of the different scan positions, as well
as the alignment of the different time steps was carried out using
permanently fixed tie objects, placed around the slope. For further post
processing – e.g. alignment of the point clouds, colouring of the point
clouds, vegetation filtering… – we used the software RiSCAN Pro v1.7.9
that comes with the TLS system. Finally processed point clouds were exported
and gridded in SAGA GIS/LIS (Rieg et al., 2014) with cell sizes of
10 × 10 cm. Further details and information concerning the TLS post processing
workflow are explained in much more detail in Haas et al. (2011a, 2012a).
In order to quantify and analyse surface changes, we applied a filtering
method according to. Using the inaccuracy of the measuring device and a
statistical t test, only significant changes were analysed. The level of
detection (LoD) under a 95 % confidence interval was calculated as 5.54 cm
LoD=tcritδ12+δ22
More detailed information on the application of the statistical t test are
explained in Lane et al. (2003), Brasington et al. (2003), Wheaton et al. (2009), and Neugirg et al. (2015).
Erosion 3D – physically based erosion modelling under alpine conditions
To account for the non-stationary nature of our research area a physically
and event based erosion model was chosen to depict the multitude of
processes adequately. All mathematical and physical equations incorporated
in the soil loss model are beyond the scope of this article but are
accessible in Schindewolf and Schmidt (2012). EROSION 3D requires basic
parameters such as rainfall, a digital terrain model and soil structural
data. Furthermore, additional inputs such as the hydraulic surface roughness
and soil resistance to erosion are derived from simulated rainfall
experiments. As the model was developed for and is usually applied on
agricultural sites data is commonly accessible from official sources.
Nevertheless, for the Lainbach valley conditions differed in various aspects
from the above: smaller size of the research area, stronger inclination,
higher bulk densities combined with large gravel quantities and – for the
winter period – snow influences and freeze-thaw cycles. Resulting from the
above and as a prerequisite for decent modelling results, adequate data
needed to be generated specifically for the site. The terrain data was
derived as a by-product from the TLS monitoring, data on soil behaviour to
heavy rainfall was produced with an artificial rainfall simulator and
on-site sampling and can be accessed in Kaiser et al. (2014).
Meteorological data input was ensured by a climate station at the slope with
rainfall data in 15 min steps (Fig. 2). The precipitation data was also
used for extracting wet and dry soil conditions in advance to a subsequent
erosive rain event. The latter were identified by filtering for events with
more precipitation than 0.25 mm min-1 or 10 mm h-1.
Precipitation for the monitored and modeled period with the
filtered events and wet soil conditions for model parametrization.
As shown in Schindewolf et al. (2015) the transfer of the model to alpine
conditions was accompanied by various challenges which could partially be
resolved in the aftermath of the initial application for the summer period.
Especially the roughness values were corrected by data from rill flushing
experiments along with tracer measurements. A levelling of surface
irregularities in the rills by runoff was accounted for by adjusting
roughness values from 0.012 for the overall slope to 0.0365 (dry) and 0.0235
(wet) for interrill areas respectively 0.0245 (dry) and 0.0095 (wet) for the
rills.
Statistical-based erosion modelling using the sediment contributing area
In order to model fluvial erosion in alpine catchments, Haas (2008) and Haas et
al. (2011b) developed a rule-based statistical model. Sediment delivery was
measured by using erosion traps in channels. These sediment delivery rates
were correlated with the size of the sediment contributing area (SCA)
upstream of the related erosion trap. Both values showed positive
correlations on a log-log plot. Neugirg et al. (2014) showed that an
adaption from catchment to hillslope scale provides promising results.
Furthermore the model was expanded with a random sampling of the chosen
virtual traps in order to get a greater variance in the sizes of the SCA
(Neugirg et al., 2015). For this study, measured erosion values for the
five month summer period were routed downslope in SAGA GIS/LIS using the
module “Catchment Area (parallel)”. Since the entire slope is without
hindering vegetation and it is steep enough, the rule-based approach for the
extraction of the SCA could be ignored. Instead the normal hydrological
catchments were used. In order to expand the statistical analysis from
Neugirg et al. (2014) we allowed the sampling algorithm to pick any grid
cell within a rill/channel. We used a random sampling to pick one grid cell
for each rill/channel. In terms of statistical independence it is important
to only pick one cell per rill. Otherwise lower lying cells in a rill are
autocorrelated with the other cells as they are directly independent from
the upwards lying cells (downslope routing of the surface changes).
Therefore we correlated the sediment yield and the size of the hydrological
catchment for 14 values and applied a linear equation according to the
sampled values. The sampling was repeated 100 times, which leads to 100
different linear equations. The linear equations are based on the Eq. (2):
log. sediment yield=intercept+slope⋅SCA
ResultsMeasured surface changes using TLS
The erosion of all grid cells with significant erosion values has a mean of
21 cm and a standard deviation of 18 cm. Erosion is mainly focussed within
the rills and at the bottom of the channels (Fig. 3). These areas show
consistent and coherent greater erosional areas. Some smaller singular
erosion patches are also at the slopes and channel walls. Furthermore almost
no erosion can be detected at the channel heads. The main erosion hot spots
are from about 1/3 of the channel length down to the slope foot.
Surface changes from TLS data.
E3d Model results
For the summer period a maximum surface lowering of 51 cm at a mean value of
15 cm at a standard deviation of 4.77 cm. Negligible deposition occurred in
a few cells on the slope bottom whilst the larger quantity of soil is
transported beyond the area of the investigated slope (Fig. 4). With regard
to a pattern in the modelled soil loss the rills show concentrations of
higher erosion value. Nevertheless, areas on the sidewalls of the incisions
also show contributing rilling forms. Compared to the TLS results the
proportion of the sidewall effects is higher, while rill incision in the
large rills is underestimated. Furthermore, a tendency of higher changes
towards to upper (western) part of the slope in the model contradicts the
TLS data, which reveals major incisions in the lower (eastern) areas.
Modelling results from E3d.
Model results of the statistical-based erosion model. Averaged
values for one month for this study (left) and for the study by Neugirg et al. (2015) (right).
Surface changes acquired with TLS (left) and E3D (right).
TLS E3D no. of cellsmean ofVolumeno. of cellsmean ofVolumewith significantchange(m3)with significantchange(m3)surface changesurface change3785-0.22 m8.5235537-0.15 m8.665
Results from the statistical based erosion model for one month.
SCASCA (Neugirgthis studyInterceptSlopeR2et al., 2015)InterceptSlopeR2min0.3360.0680.26min0.4480.0520.12median0.4320.1320.48median0.4820.1160.48max.0.5310.1910.63max0.5190.1830.89Statistical-based erosion model results
The model results show a positive correlation between sediment contributing
area and the sediment yield (Fig. 5, left side). The goodness of the
correlation is expressed as R2 for each of the 100 linear
equations (Table 3). R2 values are distributed from 0.26 to
0.63 with a median of 0.48. In order to achieve a better comparability,
intercept and slope of the linear equations was averaged for one month.
Intercept values show a range from 0.336–0.531 with a median of 0.432.
Slope values vary between 0.068 and 0.191 with a median of 0.132.
Discussion
Considering the results presented in Schindewolf et al. (2015) the spatial
and temporal distribution of manning's n roughness values showed an improved
reproducibility of the summerly slope processes. Furthermore, it was
mandatory to also apply the significant changes (LoD) of the TLS scans of
the modelling results from EROSION 3D. As erosion induced surface changes
are frequently scaled in a millimetre range, TLS results are questionable at
the lower end of the scale. Thus, the modelling could be adduced to
adequately complement the laserscan in a way that also micro-topographical
changes are included in the overall erosion budget. However, for reason of
comparability between TLS and EROSION 3D a LoD of 5.54 cm was applied on
both methodologies.
Analysing the spatial distribution of the soil loss illustrates differences
between both applications especially in the rills. The adjusted roughness
values for dry and wet conditions interacting with a fitted spatial
differentiation of rill and interrill areas improved the pattern but also
leaves potential for further advancement. As the initial model application
was limited to agricultural sites during model parametrisation, sheet flow
played an important role. This could be a reason for more detachment on the
rather even sidewall parts of the rills and less erosion in the rill's
depression lines when compared to the TLS data. As rill hydraulics are not
yet implemented in the model the active parts on the laser scans, which are
more or less limited to the lower regions of the rills, are less active in
the EROSION 3D results. This is due to the flow reaching transport capacity
which hinders further detachment while accumulated runoff, undercuttings and
turbulent flow might further boost erosion inside the rills.
The model results of the statistical-based erosion model show medium to good
correlations for 50 % of the samplings. Half of the models show higher
R2 values than 0.48. This is exactly the same median
R2 value Neugirg et al. (2015) showed for another
smaller slope in the same catchment area (Table 3, Fig. 5). However, the
range of R2 is much smaller than the values for the previous
study. In contrast, intercept and slope values are in very good agreement
with the values from Neugirg et al. (2015). This agreement is very
promising as it implies the applicability of the model from one slope to
another under same conditions (similar substrate, precipitation) in one
catchment area. Resulting differences between this (2014) and the previous
study (2009) might be due to different precipitation data, contrary aspect
of the slope and differences in the length of the slope. But these
differences show much less discrepancies than the comparison of this method
for study areas with different substrate and climatic settings (Neugirg et
al., 2015).
Conclusions
The presented results (Table 2) do show progress in soil loss modelling for the
research area for fluvial erosion during the summer, but also leave room for
improvement in spatial distribution. By ignoring the winter period we
avoided the phase of highest activity in the catchment and thus excluded
several variables that favour non-stationarity. This was a result of
limitations in modelling that became evident when comparing TLS-measured
erosion rates to modelled ones for the winter period analysed in Schindewolf
et al. (2015). Future research will tackle the winter period including
freeze-thaw cycles, solifluction and snow-triggered processes.
Rill processes are not yet implemented in the model and need to be tackled
for a suitable reproduction from the spatial distribution point of view.
Nevertheless, an adjustment of roughness values led to better results in
comparison to the TLS data. While the grid resolution increased rapidly from
20 × 20 m2 to now 10 × 10 cm2 the model parameters
do not yet meet the demands of the new high resolution environment.
Individual processes need to be measured and analysed more precisely after
the change in scale with rill behaviour and hydraulics being of major
importance. Regarding the fact that the modelling approach was implemented
to reproduce and thus forecast soil losses for comparable slopes, the
significant agreement between total soil losses from both methods is a step
forward.
Predictions for future erosion volumes can not be made yet. A first step
towards a prediction is the analysis and quantification of each single
geomorphological process and its contribution to the annual sediment budget.
First promising results and a clear differentiation between winter and
summer processes show the studies of Schindewolf et al. (2015) and Neugirg
et al. (2015). Nevertheless, a separation of all processes is
necessary. Therefore, for future studies a decrease of the level of
detection is absolutely crucial. Especially very small processes and minor
surface changes that often occur, even during lower intensity rain falls,
cannot be detected with the present LoD calculations.
Acknowledgements
This work was supported by the German Research Foundation (DFG grant
numbers: HA5740/3-1, SCHM1373/8-1). Furthermore the authors want to thank
Alena Huber and Paula Hilger for help with the precipitation data as well as
the students Manuel Stark, Christian Böhm and Nico Schultze for their
valuable help during field work and in the laboratory.
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