Mainly in the context of global climate change the awareness of landslide hazards has risen considerably in most mountainous regions worldwide in the last years. National and regional hazard mapping programs were set up in many countries and most of the potentially endangered sites have been identified. Although exclusive geodetic and geotechnical instrumentation is available today, due to some economical reasons only few of the identified potentially risky landslides are monitored permanently. The intention of the alpEWAS research project is to develop and to test new techniques suitable for e‰cient and cost-e¤ective landslide monitoring. These techniques are combined in a geo sensor network with an enclosed geo data base and a developed software package to use the whole system for stakeholder information and early warning purposes. The core of the project is the development and testing of the three innovative measurement systems time domain reflectometry (TDR) for the detection of subsurface displacements in boreholes and reflectorless video tacheometry (VTPS) and a low cost GNSS sensor component for the determination of 3D surface movements. Essential experiences obtained during the project will be described.

Abstract. Deep-seated landslides are an important and widespread natural hazard within alpine regions and can have significant impacts on infrastructure. Pore water pressure plays an important role in determining the stability of hydrologically triggered deep-seated landslides. Based on a simple tank model structure, we improve groundwater level prediction by introducing time lags associated with groundwater supply caused by snow accumulation, snowmelt and infiltration in deep-seated landslides. In this study, we demonstrate an equivalent infiltration calculation to improve the estimation of time lags using a modified tank model to calculate regional groundwater levels. Applied to the deep-seated Aggenalm landslide in the German Alps at 1000-1200 m a.s.l., our results predict daily changes in pore water pressure ranging from −1 to 1.6 kPa, depending on daily rainfall and snowmelt, which are compared to piezometric measurements in boreholes. The inclusion of time lags improves the results of standard tank models by ∼ 36 % (linear correlation with measurement) after heavy rainfall and by ∼ 82 % following snowmelt in a 1-2-day period. For the modified tank model, we introduced a representation of snow accumulation and snowmelt based on a temperature index and an equivalent infiltration method, i.e. the melted snow-water equivalent. The modified tank model compares well to borehole-derived water pressures. Changes of pore water pressure can be modelled with 0-8 % relative error in rainfall season (standard tank model: 2-16 % relative error) and with 0-7 % relative error in snowmelt season (standard tank model: 2-45 % relative error). Here we demonstrate a modified tank model for deep-seated landslides which includes snow accumulation, snowmelt and infiltration effects and can effectively predict changes in pore water pressure in alpine environments.

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In context of global climate change and the continuous extension of settlement areas in the Alps, especially due to tourism, an increasing conflict can be observed between land use and natural hazard prevention. This also includes deep‐seated landslides, which can cause considerable damage to settlements and infrastructure when they occur and even endanger lives. The hazard potential of slow deep‐seated landslides has often been underestimated up till now. For economic reasons, such potentially dangerous instable slopes often are only monitored sporadically if at all. The alpEWAS project (“development and testing of an integrative 3D early warning system for instable alpine slopes”) is currently developing a low cost 3D monitoring and early warning system for landslides based on three innovative continuous measurement systems for underground and surface deformations: Time Domain Reflectometry, reflectorless video tacheometry and low cost global navigation satellite system. These are merged with other sensors, which monitor typical trigger mechanisms (e.g. precipitation), into a geo sensor network, providing remote online access to all data in near real time in a WebGIS environment. The alpEWAS system has been installed at the Aggenalm Landslide for a first field test. The experiences made there will be of great importance for the medium‐term goal: the development of a market‐ready, flexible and economic early warning system for landslides.