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To improve the understanding and the modelling of soil water regimes in alpine areas it is essential to know not only the number and the properties of the soil layers, but also their spatial distribution. One common assumption used in many simulations in the literature is that that the soil layers and the bedrock are parallel to the surface, which sometimes diverges significantly from the reality. A field campaign was conducted in the Urseren Valley, which lies in the heart of the Swiss Central Alps. This region is very susceptible to infiltration-triggered shallow landslides and for this reason a realistic simulation of soil water regime is crucial to predict soil slip occurrences. The primary method used for the determination of subsurface topography is the Ground Penetrating Radar at 100MHz and 250 MHz frequency. A 2D processing and analysis was carried out on the data collected form the field. Additional trenches were dug up at strategically important points to verify the soil stratigraphy obtain from the GPR analysis. Furthermore, soil samples were collected and tested in order to obtain some soil properties of the soil layers composing the profiles. These were used in a model based on Cellular Automata for the simulation of unsaturated and saturated flow. Simulations were run using representative rainfall events as recorded at the neighboring station of Andermatt. The results reveal clearly the importance of the detailed knowledge of the subsurface topography in such types of simulations.
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Theroleof subsurface topographyand its implicationson thewaterregime in theUrserenValley,Switzerland
Grigorios G. Anagnostopoulos1 , Stefan Carpentier2, Markus Konz1 , Ria Fischer2 and Paolo Burlando1
Institute of Environmental Engineering (1), Institute of Geophysics (2)
IntroductionTo improve the understanding and the modellingof soil water regimes in alpine areas it is essentialto know not only the number and the properties ofthe soil layers, but also their spatial distribution.One common assumption used in many simula-tions in the literature is that that the soil layers andthe bedrock are parallel to the surface, which some-times deviates significantly from the reality.
Field CampaignA field campaign was conducted in the UrserenValley, which lies in the heart of the Swiss CentralAlps. This region is very susceptible to infiltration-triggered shallow landslides and for this reason arealistic simulation of soil water regime is crucial topredict soil slip occurrences. The primary methodused for the determination of subsurface topogra-phy is the Ground Penetrating Radar. Additionaltrenches were dug up at strategically importantpoints to verify the soil stratigraphy obtained fromthe GPR analysis.
GPR data acquisitionIn order to obtain information about the possiblesliding planes of the soil slips, a total of 36 GPRprofiles were acquired (20 x 100 MHz data, 16 x 250MHz data). Trace spacing of the 100 MHz data was20 cm, and 5 cm for the 250 MHz data. DifferentialGPS enabled +- 3 cm precise absolute coordinatesfor each trace.
GPR data intepretationThe 100 MHz data achieved deeper signal penetra-tion than the 250 MHz, at the cost of lower res-olution. Both data have however imaged similarstructures, confirming the reliability of the latter. Ingeneral, strong subsurface topography is observedin some major interfaces. Due to sudden lateralchanges in GPR depth penetration and a consistentobservation in several trenches, it is assumed thatone of the major interfaces is a clay layer. This claylayer is undoubtedly an erosional product of thePermian schist bedrock, and very probably acts asa sliding plane for the soil slips, under the influ-ence of water flow patterns.
Hydrological simulationsThe soil profiles obtained from the GPR analysiswere used in a model based on Cellular Automatafor the simulation of unsaturated and saturatedflow. Soil samples were collected and tested in or-der to obtain some soil properties of the soil lay-ers composing the profiles. Simulations were runusing representative rainfall events as recordedat the neighboring station of Andermatt. TheIDF (Intensity-Duration-Frequency) curves wereextracted using the historical record of the Ander-matt station. 3-day events are selected from thisrecord, which have the same total amount of pre-cipitation as a constant intensity event with the in-tensity given by the IDF for different return peri-ods. Three events were used with return periodsT = 2, T = 5, and T = 15 years.
ResultsWe run the numerical model for seven profiles, using two different stratigraphy configurations. First, weused the the interfaces from the GPR interpretation and afterwards we used the assumption that the soillayers are parallel to the surface. The results clearly reveal the importance of the detailed knowledge ofthe subsurface topography in such types of simulations. In the next two figures the differences in pressurehead between the GPR profile and the parallel layer profile are presented.
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Difference in pressure head (m) between GPR profile and parallel layer profile Profile 1, T=5
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Difference in pressure head (m) between GPR profile and parallel layer profileProfile 4, T=15
At the figure on the right the empirical histogramand empirical cumulative distribution function ofthe percentage difference between the GPR andparallel layer profiles are presented. It is obviousthat around 40% of the cells across all the simu-lated profiles for the three different rainfall eventshave pressure differences that vary between 25%and 200%. These cells can create "bottle-neck" ef-fects, where zones of high pressure head are ob-served. A local increase in the pore water pressurecan significantly decrease the shear strength of theregion and could initiate a slope failure. These re-sults show that the role of subsurface topographyis not at all negligible especially in phenomenonwhere the fluctuations of the water regime arevery important (e.g. rainfall-induced landslides). −200 −100 0 100 200
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References[1] G.G. Anagnostopoulos, P. Burlando, (2011). Object-oriented computational framework for the simulation of variably saturated
flow, using a reduced complexity model, Submitted in Environmental Modelling & Software[2] N.J. Cassidy, (2009). Ground Penetrating Radar data processing, modelling and analysis, Ground Penetrating Radar Theory and
Applications, p. 141-176, Elsevier, Amsterdam
