Interactions of Hydrometeorological Processes and Debris-Flow Activity in Two Alpine Catchments

Interactions of hydrometeorological processes and debris-flow activity in two Alpine catchments

Olivia Debora Sartorius

Department of Physical Geography GE9009 Degree Project in Physical Geography and Quaternary Geology 30 credits, NKA 239 Master's Programme in Hydrology, Hydrogeology and Water Resources (120 credits) Autumn term 2019 Supervisor: Andrew Framton

Preface

This Master’s thesis is Olivia Debora Sartorius’s degree project in Physical Geography and Quaternary Geology at the Department of Physical Geography, Stockholm University. The Master’s thesis comprises 30 credits (one term of full-time studies).

Cooperation with Swiss Federal Institute for Forest, Snow and Landscape Research (WSL).

Supervisors have been Andrew Frampton at the Department of Physical Geography, Stockholm University as well as Jacob Hirschberg at WSL. Examiner has been Britta Sannel at the Department of Physical Geography, Stockholm University.

The author is responsible for the contents of this thesis.

Stockholm, 1 September 2020

Björn Gunnarson Vice Director of studies Supervision Andrew Frampton, SU Jacob Hirschberg, WSL

Master in Hydrology, Hydrogeology and Water Resources Department of Physical Geography, Stockholm University

In collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL

2 Abstract

Globally, debris flows are one of the most destructive types of natural hazards. Apart from geomorphological conditions, hydrometeorological processes are decisive for the debris flow initiation. So far, hydrometeorological conditions have mainly been studied in large-scale study areas. This thesis focuses on the Dorfbach and the Ritigrabe torrents, which are both located in the Southwestern Swiss Alps. Through establishing rainfall intensity-duration thresholds and the use of the hydrological module of the probabilistic sediment cascade model SedCas, investigations on the hydrological state present before the debris flows as well as on the triggering hydrometeorological processes were done for a time period of 34.5 years. The rainfall intensity-duration threshold curves with the best predictive performance were obtained by � = 1.7 ∗ �. for the Dorfbach and by � = 10.6 ∗ �. for the Ritigrabe, where D is rainfall duration in hours and I is the mean rainfall intensity in mm/h. The debris-flow trigger classifications indicated that long-lasting rainfall and rain-on-snow events were responsible for the majority of the debris-flow initiations in both catchments. In the Dorfbach 15 debris flows were triggered by long-lasting rainfall and 12 debris flows were related to rain-on-snow events. In the Ritigrabe, four debris flows were initiated by long-lasting rainfall and five of the nine occurring debris flows happened because of rain-on-snow events. Intense snowmelt and short- duration storms could only trigger one, respectively six debris flows in the Dorfbach. It could be observed that if the water storage was already filled up the additional water input which was needed for the debris-flow initiation was only little. The results suggest that many different hydrometeorological conditions can lead to debris-flow initiation. For the first time in the study areas, information on rainfall characteristics promoting debris-flow initiation is provided through the use of intensity-duration thresholds. However, the results also highlight the importance of including hydrometeorological processes such as snowmelt and antecedent soil moisture for the recognition of clearer patterns in the debris-flow activity.

3 Content

Abstract 1. Introduction ...... 1 1.1 Aims of the study ...... 2 2. Methods ...... 3 2.1 Study sites ...... 3 2.1.1 Dorfbach, Randa ...... 4 2.1.2 Ritigrabe, St. Niklaus ...... 5 2.2 Data sources and processing ...... 6 2.2.1 Meteorological data ...... 6 2.2.2 Debris flow data ...... 7 2.3 Intensity-duration thresholds ...... 9 2.4 SedCas model, hydrological module ...... 9 2.4.1 Snow module ...... 10 2.4.2 Water balance and calibration ...... 11 2.4.3 Validation of the hydrological module from SedCas ...... 12 2.5 Debris-flow trigger classification ...... 13 3. Results ...... 15 3.1 Intensity-duration thresholds ...... 15 3.1.1 Dorfbach, Randa ...... 15 3.1.2 Ritigrabe, St. Niklaus ...... 16 3.1.3 Comparison with established rainfall-intensity thresholds ...... 16 3.2 SedCas modelling ...... 17 3.2.1 Dorfbach, Randa ...... 18 3.2.2 Ritigrabe, St. Niklaus ...... 19 3.3 Debris-flow trigger classification ...... 20 3.3.1 Dorfbach, Randa ...... 21 3.3.2 Ritigrabe, St. Niklaus ...... 24 4. Discussion ...... 29 4.1 Uncertainties in input data ...... 29 4.2 Intensity-duration thresholds ...... 29 4.3 SedCas – the hydrological module ...... 31 4.4 Debris-flow trigger classification ...... 31 4.5 The role of rock glaciers as sediment source ...... 32 4.5.1 Dorfbach, Randa ...... 33 4.5.2 Ritigrabe, St. Niklaus ...... 33 5. Conclusions ...... 34 6. Acknowledgments ...... 35 References ...... 36 Appendix ...... 40

4 1. Introduction

Mountainous regions are susceptible to many different natural hazards like avalanches, landslides, debris flows, floods and rockfalls (Badoux et al., 2009). Debris flows represent one of the major hazards (Mostbauer et al, 2018; Prenner et al., 2018a). In the years from 1982 to 2018, debris flows were responsible for a financial damage of 531’116’460 Euros in Switzerland alone (WSL1, 2020; Hilker et al., 2009).

In the late 1960s, scientists began to explore the mechanisms of debris flows (Takahashi, 2014). The initiation of a debris flow is dependent on the sediment and water availability (Bennett et al., 2014). Debris flows are driven by gravity and transport sediments, which are comprised of various grain sizes such as clay and large boulders. Moreover, debris flows can have a great erosion capacity, which implies an even higher hazard potential (Takahashi, 2014; Rickenmann & Zimmermann, 1993).

In general, debris flows are triggered by long-lasting or short and intense rainfall events (Guzzetti et al., 2007), intensive snowmelt (Prenner et al., 2018a&b), a sudden outburst of a lake, earthquakes or volcanic eruptions. Debris flows are also characterized by rapid flow velocities varying between 0.5 m/s and 20 m/s (Takahashi, 2014) in the course of steep channels (Coviello et al., 2019). Their flow is typically unsteady as presented in Figure 1. One event can embody one or several pulses. Between the pulses, fluvial sediment transport can occur (Rickenmann & Zimmermann, 1993). Depending on the debris flow, the mass moves until the slope turns flatter than 3-30° and the transported material is deposited in a debris-flow fan (Takahashi, 2014). Other typical features of debris flows are the formation of levees along the flow path and poorly sorted deposits (Rickenmann & Zimmermann, 1993).

Nowadays, debris-flow fans increasingly serve as areas for buildings and transportation infrastructure. This, in turn, means a growing potential financial damage and an increasing need for a robust debris-flow forecast (Coviello et al., 2019). However, forecasts are only as good as the background knowledge they are based on. So far, research in the Alps focuses mainly on sediment transport (Badoux et al., 2012 and more) and event analyses (e.g. Rickenmann & Zimmermann 1993, Pralong et al., 2017). However, debris flows are triggered by an interplay of geomorphological but also hydrometeorological processes. Traditionally, intensity-duration thresholds are established to investigate rainfall properties of debris-flow triggering rainfall events. This can be done based on e.g. the rainfall intensity and its duration (Guzzetti et al., 2007 & 2008).

In-depth research on deciding hydrometeorological conditions which trigger debris flows has just commenced, e.g. by Prenner et al. (2018a&b) and Mostbauer et al. (2018) who analyse hydrometeorological debris-flow trigger mechanisms in large Austrian catchments. Nonetheless, the hydrometeorological conditions leading to debris flows are supposed to vary spatially due to the complex climate variations in mountain valleys. Therefore, it is important to explore these conditions in various spatial and temporal scales.

This master’s thesis was conducted in collaboration with WSL, the Swiss Federal Institute for Forest, Snow and Landscape Research. The project is part of the WSL’s strategic initiative “Climate Change Impacts on Alpine Mass Movements”, which aims to investigate the impacts of melting glaciers and permafrost thaw, which destabilize hillslopes of enormous proportions. Research is essential for the process of understanding and developing optimal adaptation strategies (WSL2, 2020). Due to climate change, debris-flow activity is expected to increase in

1 the Swiss Alps (Jakob & Hunger, 2005) because of more high-intensity rainfall events (NCCS, 2018) and greater sediment availability due to permafrost thaw and glacier retreat (Jakob & Hunger, 2005). Some studies even expect debris flows with magnitudes of no historical parallels in the Swiss Alps (Stoffel et al., 2014). Thus, it is essential to investigate and better understand the ongoing processes for the establishment of reliable debris-flow forecasts.

1.1 Aims of the study

This study aims to investigate the interactions of hydrometeorological processes and debris- flow activity in two Alpine catchments. The main focus is laid on the following questions:

• Which hydrological state is present before the occurrence of debris flows in the Dorfbach and the Ritigrabe?

• Which hydrometeorological conditions lead to debris-flow initiation in the Dorfbach and the Ritigrabe?

To address these questions, intensity-duration thresholds were determined according to Leonarduzzi et al. (2017) and set into context with other Alpine catchments. In addition to the rainfall conditions which can trigger debris-flow initiation, a seasonal subdivision is conducted to obtain insights on possible interactions with the water input through snowmelt. Next, the hydrological module of SedCas, a probabilistic sediment cascade model, is applied to investigate further hydrological parameters like snow accumulation and snowmelt, soil water storage and evapotranspiration (Bennett et al., 2014). Lastly, the modelled data is used to qualitatively classify the debris flows according to the hydrometeorological conditions which triggered debris flows. The classification distinguishes between the following trigger mechanisms: long-lasting rainfall, short-duration storms, rain-on-snow events and intense snowmelt. This study contributes to a better understanding of the specific hydrometeorological conditions which can initiate debris flows in two Swiss alpine catchments, in a periglacial environment and where debris flows are frequent. In the study catchments in many years even several debris flows per year were recorded (Pralong et al, 2017).

Figure 1 Sketch of a typical debris flow (Pierson, 1986)

2. Methods

2.1 Study sites The thesis focuses on two Alpine torrents, the Dorfbach and the Ritigrabe. Both catchments are located in the Matter valley in the Canton Valais in the southwestern Swiss Alps (Figure 2). The torrents flow into the Matter Vispa, which drains the Matter valley (swisstopo, 2020). The Dorfbach catchment, as well as the Ritigrabe catchment, are characterized by steep west-facing slopes, and by the presence of the similar periglacial features like rock glaciers, permafrost and moraines sediments (Pralong et al., 2017). Moreover, the chosen torrents have a well-recorded debris-flow history and some previous research in the region has focused on various geomorphological aspects. The debris-flow history highlights that for both torrents, debris flows could be frequently observed in the past decades with mean event volumes of 4900 m3 in the Dorfbach and 11’500 m3 in the Ritigrabe. The Dorfbach has shown the highest debris-flow activity compared to other torrents in the Matter valley during the last decades (Pralong et al., 2017). This study concentrates on the debris-flow initiation. Therefore, both catchments were specifically delineated to only contain the debris-flow initiation area.

Figure 2 Map of Switzerland (Wikipedia, 2020) with the two study catchments in the southwestern Swiss Alps (VISGIS, 2020) The climate in the debris-flow initiation areas is characterized by highest temperatures of around 10° C during summer and mean temperatures below zero °C throughout the winter months. The highest precipitation values can be measured from April until November (Figure 3). The linear distance between the Dorfbach and the Ritigrabe is 9 km (swisstopo, 2020).

Figure 3 Climate in the Matter valley at an altitude of 2400 m a.s.l. based on the meteorological data described in chapter 2.2.1

2.1.1 Dorfbach, Randa The Dorfbach torrent is located above the village Randa, 46°06’ N 7°48’ E (swisstopo, 2020). The Dorfbach torrent is fed by two gullies. One gully, located slightly north, drains the Festi glacier. In this gully, some small debris flows have occurred in the past, but with minor volumes compared to the other gully located slightly south (swisstopo, 2020 & Graf, 2013). The southern gully represents the debris-flow initiation area from where most of the recorded debris flows are supposed to or proven to have started (Graf, 2013). Therefore, the southern gully is referred to as the Dorfbach debris-flow initiation area. According to the catchment delineation made by swisstopo, the debris-flow initiation area was specified based on the topographical analysis with ArcGIS. This area covers 0.337 km2 and spans from an altitude of 1886 m a.s.l. to 2972 m a.s.l. with a mean elevation of 2404 m a.s.l.

In this study area, permafrost is expected to occur in form of isolated patches to continuous permafrost areas. From a geological point of view, the Dorfbach torrent is located in the crystalline with mainly medium-grained gneisses, muscovite and biotite slates and some talus deposits (Pralong et al., 2017). The channel travels through old moraine deposits and is covered with loose slope debris from rock failures (Graf, 2013). In the upper part, the debris-flow initiation area features a rock glacier called Grabengufer rock glacier. Most of the deposited material of the Grabengufer rock glacier originates from a deep-seated landslide above the rock glacier due to voluminous erosion over a failure scar (Kenner et al., 2014). The Grabengufer rock glacier has a length of about 600 m and a width of 100 m. Its tongue ends on an extremely steep slope section at 2400 m a.s.l. (Delaloye et al., 2013). Recently, the Grabengufer rock glacier encountered a complete destabilization with maximum surface velocities of about 10 cm/day to 40 cm/day during July 2009 and February 2010. Since this phase, the displacement rate has gradually decreased. However, the rock glacier front still shows rapid erosion (Delaloye et al., 2013). The significant release of debris by the Grabengufer rock glacier together with large boulders from rock failures in the upper part represent a great amount of sediments, which are a decisive contribution to hazardous debris flows (Kenner et al., 2014).

Meters

0 150 300 /

Figure 4 Orthophoto from swisstopo showing the Dorfbach: debris-flow initiation area (red), Grabengufer rock glacier (green), landslide (blue), failure scar (yellow) adapted from Kenner et al. (2014)

2.1.2 Ritigrabe, St. Niklaus The Ritigrabe catchment is located above the village of St. Niklaus, 46°10’ N 7°50’ E, (swisstopo, 2020). Similar to the Dorfbach catchment, the Ritigrabe catchment was newly delineated containing only the debris-flow initiation area according to the above-mentioned procedure. The debris-flow initiation area in the Ritigrabe covers 0.745 km2. The newly delineated catchment ranges from 1979 m a.s.l. up to the Gabelhorn, whose peak reaches an elevation of 3134 m a.s.l. (swisstopo, 2020) and has a mean elevation of 2593 m a.s.l.

The geology in the Ritigrabe catchment is mainly constituted of coarse fibrous eye gneisses, medium-grained gneisses, aplites, talus deposits and older moraine sediments (Pralong et al., 2017). The Ritigrabe catchment is not glaciated; however, permafrost can be found above 2400 m a.s.l., where isolated patches to continuous permafrost areas are expected. The main contribution of loose debris originates from a rock glacier, which features a front at 2600 m a.s.l. (Kenner et al., 2018) and reaches up to 2800 m a.s.l. (Lugon & Stoffel, 2010). The rock glacier has a creep velocity of 2 m per year and the eroded material flows directly into the Ritigrabe torrent. Moreover, rock failures occur periodically and represent another notable source of loose debris in the Ritigrabe channel (Kenner et al., 2018). At the rock glacier front and its rootzone, the topography is characterized by 30-40° steep slopes, strong occurrence of ice and boulder sizes of up to a few cubic meters (Kenner et al., 2017).

Meters / 0 150 300

Figure 5 Orthophoto from swisstopo showing the Ritigrabe: debris-flow initiation area (red), rock glacier (green), adapted from Kenner et al. (2017)

2.2 Data sources and processing

2.2.1 Meteorological data Neither in the Dorfbach nor the Ritigrabe catchment, a permanent meteorological station is installed. The meteorological data is collected and pre-processed in the following ways: Precipitation data is taken from the MeteoSwiss product RhiresD. RhiresD offers information on daily precipitation, namely rainfall and snowfall (the latter of which is accumulated as water equivalents), from 06:00 UTC to 06:00 UTC of the following day since 1961 (MeteoSwiss, 2020). RhiresD is a gridded product with a resolution of 1 km x 1 km and is based on the interpolation method of Frei and Schär (1998). The interpolation takes measured data from the automated weather station network and a network of rain gauges run by MeteoSwiss. Both catchments are covered by six grid cells that provide mean precipitation depths in millimeters (MeteoSwiss, 2020). Precipitation time series from 01/10/1982 to 31/12/2017 were chosen. As the debris-flow occurrence can vary throughout the day, hourly data are of interest. Therefore, the daily precipitation sums were disaggregated into hourly precipitation sums by comparing the precipitation occurrence at a meteorological station, which is located in the close-by village of Zermatt. If precipitation was measurable in Zermatt, a certain percentage of the daily precipitation sum of the grid cell of interest was assigned to the recorded hour. Additionally, information on snow depths was taken from the close-by IMIS Snow Station "Seetal 2480 m a.s.l." of the WSL Institute for Snow and Avalanche Research SLF, see Figure 6 (SLF, 2020). As the Dorfbach and the Ritigrabe are located just 9 km apart from each other (swisstopo, 2020), the snow depth data was used for both catchments. The dataset consists of intervals with a duration of 30 minutes, ranging between 01/01/1999 and 31/12/2017 (SLF, 2020).

Temperature data was taken from meteorological stations of MeteoSwiss. Monthly lapse rates characteristic for the Matter valley were obtained from Zermatt (1668 m a.s.l.) and from the Gornergrat (3129 m a.s.l.) for the Dorfbach and the station in Visp (641 m a.s.l.) for the

Ritigrabe. Temperature data from 1994 to 2018 was used to calculate the temperature/elevation gradient, which is called lapse rate per month, see Table 1 in the Appendix. The temperature data for each catchment was determined by the addition of the measured data to the according monthly lapse rate. The obtained temperature data for the Dorfbach was validated with data measured at a temporarily installed weather station close to the Grabengufer rock glacier. Global radiation data was obtained directly from the weather station Zermatt for the Dorfbach and from the weather station Visp for the Ritigrabe.

Figure 6 Overview of the locations of the weather stations Visp, Zermatt, Gornergrat from

MeteoSwiss (red) and the snow measuring station IMIS Seetal0 from2 4 SLF6km (blue), swisstopo Massstab 1: 300,000 Gedruckt am 26.01.2020 12:48 (2020) https://s.geo.admin.ch/870d098839 www.geo.admin.ch ist ein Portal zur Einsicht von geolokalisierten Informationen, Daten und Diensten, die von öffentlichen Einrichtungen zur Verfügung gestellt werden Haftung: Obwohl die Bundesbehörden mit aller Sorgfalt auf die Richtigkeit der veröffentlichten Informationen achten, kann hinsichtlich der inhaltlichen Richtigkeit, Genauigkeit, Aktualität, Zuverlässigkeit und Vollständigkeit dieser Informationen2.2. keine Gewährleistung2 Debris übernommen flow datawerden.Copyright, Bundesbehörden der Schweizerischen Eidgenossenschaft. http://www.disclaimer.admin.ch © swisstopo, public.geo.admin.ch The Dorfbach and the Ritigrabe catchment both have a well-recorded debris-flow event history. The events were documented by the concerned villages, the canton of Valais, engineering companies and researchers (Pralong et al., 2017). The given information of each event varies. Some events are described by a potential cause and a magnitude, whereas of others it was simply recorded that an event had occurred. The presented debris-flow history does not contain information on precise event times. For previous research (e.g. Jacquemart et al., 2017), geophones were installed along the Dorfbach channel. For the present study, seismic data from 2010 to 2017 was analyzed to identify the exact time of the debris-flow initiation with the knowledge that a debris flow causes an intense seismic signal with a duration of several minutes to several hours (Coviello et al., 2019).

Figure 7 Timeline debris flows in the Dorfbach based on Pralong et al. (2017)

Figure 8 Timeline debris flows in the Ritigrabe based on Pralong et al. (2017)

2.3 Intensity-duration thresholds Rainfall is a decisive factor for debris-flow initiation (Prenner et al., 2018a). Therefore, it is of interest to define rainfall thresholds which can trigger a debris flow and to investigate which rainfall conditions are insufficient to trigger a debris flow. First, one has to analyze the rainfall characteristics which facilitate a debris-flow initiation. The debris-flow events were divided into debris-flow triggering events and non-triggering events. In the next step, the rainfall thresholds were calibrated to obtain a correct prediction of debris-flow triggering and non- triggering events. This calibration was done by an objective maximization of the predictive performance (Leonarduzzi et al., 2017). The predictive performance was quantified by the sensitivity of the determined rainfall threshold, which is based on the intensities and durations of the rainfall events. A good predictive performance means a correct prediction of triggering and non-triggering rainfall events (Leonarduzzi et al., 2017). The rainfall-threshold model used in the present study was set up according to Leonarduzzi et al. (2017).

Rainfall events are considered as independent if there is no rainfall for three hours. For each rainfall event, the following parameters were computed: start time, end time, duration, mean intensity [mm/h], maximum intensity [mm/h] and the rainfall depth [mm]. In the next step, the rainfall events were divided into debris-flow triggering and non-triggering events. The previously computed rainfall parameters were compared to the debris-flow history. A rainfall event was classified as a debris-flow triggering event if at least one debris flow occurred during the rainfall event or on the following day. Otherwise, the rainfall event was classified to be non- triggering. The following day is considered as well, because the daily rainfall data set RhiresD provides data until 06:00 a.m. of the next day. Consequently, rainfall on the following day may contribute to soil wetting and a potential debris-flow initiation. A triggering rainfall event was determined to start with the first measured rainfall and to end when the debris flow occurred, if the exact debris-flow starting time is known (Leonarduzzi et al., 2017).

Next, a rainfall threshold which divides the events into debris-flow triggering and non- triggering events was generated. For this, the true and false positive and the true and false negative events of the rainfall threshold model were counted and the prevalence-independent goodness of fit, which shows how well the threshold fits the observed debris flows, was computed. It is important to apply prevalence-independent statistical measures, like sensitivity and specificity, as the data sizes of debris-flow triggering, and non-triggering rainfall events are different. The rainfall threshold model was calibrated with the use of True Skill Statistic (TSS). The TSS shows the difference between the true positive rate and the false alarm rate �� = �� − (1 − ��)), whereas the true positive rate corresponds to the sensitivity (se) and the false alarm rate to 1- specificity (sp). The TSS tests the rainfall model performance. A random guess means that TSS equals 0; the best possible performance is achieved when TSS equals 1 (Leonarduzzi et al., 2017). Here, the model calibration was done by the maximization of the TSS for an intensity-duration threshold curve for triggering events in the form of � = � ∗ � (Caine, 1980), where D is rainfall duration in hours and I is the mean rainfall intensity in mm/h. This indicates that the threshold results in the best predictive performance in terms of the TSS. The advantage of prevalence-independent statistical measures like sensitivity or specificity is that even if the sample sizes of debris-flow triggering and non-triggering events differ greatly, the rainfall threshold model is not biased towards the more prevalent non-triggering rainfall events (Leonarduzzi et al., 2017).

2.4 SedCas model, hydrological module Intensity-duration thresholds are based on rainfall and its characteristics. Still, this approach does not account for antecedent soil moisture and snowmelt, which are also supposed to have a certain or even a decisive influence on debris-flow initiation (Mostbauer et al., 2018). To 9 include more relevant hydrological processes that influence the runoff behavior, namely precipitation, evapotranspiration, snow accumulation, snowmelt and soil moisture, the SedCas model was used for further analyses. SedCas is a probabilistic sediment cascade model which can simulate sediment transfer in a mountain basin on an hourly scale. The conceptual model understands the fluvial system as a cascade of connected reservoirs, in which sediment is transported through different cycles of storage and remobilization by surface runoff (Bennett et al., 2014). A linear reservoir model is stationary in time and assumes that a unit of input always results in the same output response (Beven, 2012). It is lumped in space. SedCas is comprised of two parts: the hydrological model and the sediment model. The hydrological module simulates the complete water balance of the study catchment as a linear reservoir with all hydrological processes which lead to surface runoff. The sediment part of SedCas, in turn, calculates the sediment discharge from landslides to hillslopes and channels, including the simulated runoff of the sediment transport (Bennett et al., 2014). The present study focuses on the hydrological model of SedCas, which features an hourly resolution. The water storage inputs are rainfall and snowmelt, the reservoir outputs are runoff and evapotranspiration. Precipitation is divided into liquid and solid phases through a temperature threshold. The snowpack is simulated with a degree-day model.

2.4.1 Snow module The prediction of the snow depth is described by a function of elevation, precipitation, temperature and a constant melt factor. Snow accumulates through cumulated precipitation events if the temperature T is below the temperature threshold T° for snow accumulation. If the temperature exceeds the temperature threshold for snowmelt, the snow melts at a rate proportional to the temperature: s(t) = mrate(T-T°), where s is snowmelt and mrate is melting rate. The snow module was calibrated with the snow data of the IMIS Snow Station "Seetal 2480 m a.s.l.". First, the snow depth data was converted into snow water equivalents (SWE) (Bennett et al., 2014). For this, a constant snow density of 0.3 g/cm3 was chosen. This snow density is based on measurements taken at the close-by Arolla glacier as well as the assumption that old and new snow equally contribute to the snowpack (Carenzo et al., 2009). SedCas calculates snow depths based on a function of temperature, precipitation, the melting rate, the temperature thresholds for snow accumulation and snowmelt, the initial snow depth and the albedo of snow and soil. For both catchments, the initial snow depth is set to 0 mm, the albedo of snow to 0.65 and the albedo of soil to 0.25 (Brutseart, 2005) (see Table 1). The snow module is calibrated by comparing the measured snow data to the simulated snow data. The three parameters mrate, T1 and T2 are calibrated by minimizing the root mean square error of the simulated and observed snow depth time series. To this end, different parameter combinations were generated by randomly sampling parameter values within certain ranges.

Table 1 Parameters taken for both catchments

Parameter Value Albedo snow 0.65 Albedo soil 0.25 Net evapotranspiration rate snow and ice 0.25-0.5 mm/d Net evapotranspiration rate debris/rock 0.5-1 mm/d Range for mrate 0.01 – 0.5 Range for temperature threshold for snow -0.5 – 2.5 °C accumulation Range for temperature threshold for -0.5 – 2.5 °C snowmelt

2.4.2 Water balance and calibration The water balance was simulated based on the following procedure: the capacity of soil and of weathered bedrock to store and discharge water was represented by the water storage (Eq. 1). Inputs were rainfall and snowmelt, outputs runoff and evapotranspiration:

�� = �(�) + ��(�) − ��(�) − �(�) (1) ��

In equation (1), Sw is water storage, P is rainfall, SM is snowmelt, AET is evapotranspiration and Q is runoff. The unit of the input data is millimeters.

The potential evapotranspiration (PET) was calculated according to Priestley-Taylor (Priestley & Taylor, 1972) with the use of the following data sets: temperature, global radiation, albedo and the mean catchment elevation. Theoretically, cover data is needed for the model. However, the measured data set already included this information. For the water balance part, the same albedo values as for the snow calibration were taken. The annual actual evapotranspiration (AET) was determined as follows: in the nearby Rhone catchment at an elevation of 2300-2500 m a.s.l., Bernath (1991) observed net evapotranspiration rates of 0.25-0.5 mm/d over snow and ice and 0.5-1mm/d over surfaces covered with rock or debris. The study catchments newly delineated for this thesis featured almost no vegetation, which is why evapotranspiration rates for vegetation were not considered. Based on the time series of the IMIS snow measurement station and the determination that a snow day requires a sum of daily snow depths of >50mm, both the Dorfbach and the Ritigrabe had 231 snow days. Consequently, the number of snow days was multiplied with the net evapotranspiration rate over snow and the remaining days were multiplied with the net evapotranspiration rate over rock and debris. For the net evapotranspiration rate over snow, 0.35mm/d was chosen; for the rate over rock and debris, the minimum value of 0.5mm/d was selected, as the torrent systems are both steeps presumably featuring fast runoff. This results in a total annual actual evapotranspiration (AET) of 147.75 mm for the Dorfbach and the Ritigrabe.

SedCas models the actual evapotranspiration as a fraction of the potential evapotranspiration (PET). AET = ¡*PET, where ¡ is an efficiency parameter that is determined as follows:

¡ = 1 − e() (2)

In equation 2, a determines how evapotranspiration is limited by the water availability in the subsurface at the potential rate. For this study, a = 0.2 mm-1 was chosen according to Bennett et al. (2014). The parameter alpha, which is the evapotranspiration efficiency parameter, the parameter for the water storage capacity, Vwcap [mm] and the residence time (ks) were calibrated. Also, these parameters were random estimations which had to be tested by testing good parameter combinations (Table 2). It was determined that the hydrological model should consist of two layers. In the lower layer, the water could be stored in fractured bedrock or coarse sediment deposits. In contrast, it is assumed that the upper layer mainly consists of loose debris. The residence time (ks) describes the runoff attenuation through subsurface flow paths and reservoirs. The residence time varies for each layer: the lower is supposed to have longer residence times, while runoff is supposed to occur faster in the upper layer because of the described layer properties.

Table 2 Initial uniformly distributed parameter estimation ranges applied for both catchments

Parameter Value Alpha 1 - 20 Ks1 1 - 168 Ks2 96 - 336 Vwcaps1 5 - 100, condition >50 Vwcaps2 5 - 100, condition < 70

The calibration for the best parameter combination was conducted by the minimization of the absolute error of the difference between the simulated AET and the observed AET. After a first calibration, the system produced almost no runoff, even though precipitation was occurring, which is not possible as the reservoir is not infinitely large. Comparison with the rainfall coefficients of the rainfall threshold analysis indicated that the water storage capacity of the catchments should be around 60 mm in total. With this condition, the parameter calibration was improved. Yet, it is important to consider that for further calculations, only one combination of one best solution was applied. It is likely that further equally good parameter sets exist, which could influence further calculations up to a certain level. The water storage reservoir releases runoff, if the water storage capacity is exceeded. All excess water coming from snowmelt or/and rainfall is discharged into the torrent and out of the catchment. If the storage capacity is not exceeded, runoff is simulated as a function of the already stored water with the assumption of a linear reservoir relation (Eq. 3):

�(�) = { ��(�)�� (�) < �� or Sw(t)-Vwcap if Sw(t) ³ Vwcap} (3)

2.4.3 Validation of the hydrological module from SedCas After the parameter calibration of the snow module, a validation was conducted to verify the model performance of the entire hydrological model part (Biondi et al., 2011) and to obtain more information on possible limitations of the model. The validation was based on the comparison of the simulated parameter output of the annual mean actual evapotranspiration between two subsections of the entire time series. Under the assumption of a stationary climate, the parameter sets from the snow calibration were included in the hydrological model for simulations for the time between 01/01/1983 and 31/12/1999 as well as between 01/01/2001 and 31/12/2017. For both 16-year periods, the annual actual evapotranspiration was studied, since the whole hydrological module is ultimately calibrated based on the actual evapotranspiration. As the validation for both catchments showed reasonable results, the parameter sets were further used to calibrate the complete hydrological model. Tables 2 and 3 present an overview of the parameter values determined for each catchment.

Table 3 Parameter values for SedCas, Dorfbach

Parameter Value Debris-flow initiation area 0.337 km2 melting rate 0.5 mm/°C*h Temperature-threshold for snow 2.4°C accumulation Temperature-threshold for snowmelt 2.4°C RMSE 13.24 mm/h Alpha 4.13 KS1 25.95 h KS2 200.92 h Vwcap1 6.60 mm Vwcap2 53.45 mm

Table 4 Parameters for SedCas, Ritigrabe

Parameter Value Debris-flow initiation area 0.745 km2 melting rate 0.23 mm/°C*h Temperature-threshold for snow 2.4 °C accumulation Temperature-threshold for snowmelt 0.9 °C RMSE 34.18 mm/h Alpha 1.19 KS1 85.35 h KS2 255.16 h Vwcap1 35.51 mm Vwcap2 32.39 mm

2.5 Debris-flow trigger classification Debris flows can be triggered by various factors. It is assumed that specific hydrometeorological conditions can favour the initiation of debris flows. For this analysis, the event history data set and the parameters of the SedCas hydrological model were used to specifically test whether a debris flow was triggered by a long-lasting rainfall event (LLR), by a short-duration storm (SDS), by a rain-on-snow event (ROS) or by intense snowmelt (SM). Long-lasting rainfall events gradually lead to increasing soil moisture. Simultaneously, the evapotranspiration occurs at lower rates as global radiation is reduced due to the cloud cover, and temperatures often decrease (Häckel, 2016). In contrast to long-lasting rainfall events, short-duration storms are associated with high antecedent evapotranspiration rates because of the high solar energy input, which is possible as the cloud cover is comparatively negligible prior to the rainfall event. This, in turn, leads to decreasing soil moisture contents (Prenner et al., 2018b). Another sign of short-duration storms is a low temporal fraction of precipitation during the studied 48 hours before the debris flow event, especially in connection with high maximum precipitation sums. If it rains on snow, the snowpack properties change in various ways depending on the snowpack properties before the rainfall. Rainfall on snow can enhance snowmelt, which altogether leads to increased runoff generation (Würzer et al., 2016). Snowmelt increases the susceptibility to debris-flow initiation, as it reduces the additional water needed to trigger an event (Mostbauer et al., 2018). To test whether a LLR, a SDR, a ROS or SM triggered the observed debris flows, measured and modelled parameters were used for the

13 analysis. The classification of the trigger mechanism for each debris flow was made by comparing the percentiles and the corresponding minimum values to hydrographs according to the above-mentioned criteria. The parameters of interest are presented in Table 5:

Table 5 Parameters chosen from the SedCas simulation as a support for the debris flow classification

Parameter Abbreviation Snowmelt gradient sm_g Total snowmelt sm_t Evapotranspiration gradient pet_g Total evapotranspiration pet_t Precipitation gradient p_g Total precipitation p_t Maximum precipitation p_max Temporal fraction of precipitation fr Water storage gradient vw_g Total water storage vw_t

3. Results

This chapter addresses the following research question: “Which hydrometeorological conditions facilitate debris-flow initiation in the Dorfbach and the Ritigrabe?” First, intensity- duration thresholds were analysed to obtain specific rainfall characteristics, which may initiate debris flows in one or the other catchment. With the simulations of the hydrological module from the SedCas model, more hydrometeorological processes could be analysed and thus a classification of the debris-flow triggering processes established. Depending on the season, different debris-flow triggers were dominant.

3.1 Intensity-duration thresholds

3.1.1 Dorfbach, Randa During the study period of 1/10/1982 to 31/12/2017, 34 debris flows were recorded in total. In the Dorfbach, more non-triggering rainfall events occurred than debris-flow triggering events. The triggering rainfall events had mean intensities of 0.1 to 6.3 mm/h and durations ranging from 1 to 22 hours (Figure 9). The intensity-duration threshold model calibration, done by the optimization of the TSS, resulted in a maximum TSS value of 0.36. As a result, the optimal intensity-duration curve was characterized by � = 1.7 ∗ �.. The sensitivity was 0.59 (indicating that 41% of the debris flows were not accounted for) and the specificity 0.77 (indicating 23% of alarms, which were false). Most of the debris flows, namely 20, occurred in May and June. 12 debris flows were observed in the summer months of July and August. In early autumn, during September and October, the Dorfbach experienced only two debris flows. These debris flows were initiated by high intensities and middle- to long-duration rainfall events. In comparison, the debris flows recorded during late spring or in the summer months were triggered by various rainfall intensities and durations.

Figure 9 Intensity-duration threshold with a seasonal classification of the debris flow in the Dorfbach

3.1.2 Ritigrabe, St. Niklaus In the Ritigrabe torrent, nine debris flows were detected between 1/10/1982 and 31/12/2017. The triggering rainfall events were characterized by rainfall intensities ranging from 0.1 up to 6.6 mm/h and rainfall event durations between 3 and 18 hours. The maximum value of the TSS was 0.46. The corresponding sensitivity was 0.89 and the specificity was 0.57. The model calibration produced an optimal intensity-duration threshold curve that is described by � = 10.6 ∗ �.. Figure 10 gives an overview of the seasonal distribution of the recorded debris flows. One debris flow occurred in late May, six debris flows happened in July and August and two debris flows were initiated in the early autumn in September and October. In the case of the Ritigrabe, the triggering rainfall events had varying characteristics during the study period. Analysis showed that all of the observed debris flows in the Dorfbach and the Ritigrabe occurred between May and October.

Figure 10 Intensity-duration threshold with a seasonal classification of the debris flow in the Ritigrabe

3.1.3 Comparison with established rainfall-intensity thresholds Comparing Figure 11 to further study results sets the Dorfbach and the Ritigrabe into context with other studies. Zimmermann (1997) analysed debris flows in various Swiss catchments and considered the Illgraben, with its well-equipped observation station, to be one of the most active debris-flow torrent systems in the European Alps (Badoux et al., 2008). Compared to other intensity-duration curves, the Dorfbach has a flat curve. At the same time, however, independent of the rainfall duration high rainfall intensities are needed to trigger debris flow. The curve for the Ritigrabe is the steepest. For the same rainfall intensity, debris flows are triggered much earlier in time in the Ritigrabe than in the Dorfbach torrent. The intensity- duration curve of Zimmermann (1997) is characterized by the highest required rainfall intensities. This is due to only extreme rainfall events being considered.

Figure 11 Comparison of the intensity-duration thresholds of this study with established thresholds from Zimmermann (1997) and Hirschberg (2019) Overall, these results indicate that in case of a short rainfall event, the Dorfbach is more susceptible to a debris-flow initiation than the Ritigrabe is, because the Ritigrabe requires higher rainfall intensities to respond with a debris flow. Opposing observations were obtained for rainfall events with a long duration, but low rainfall intensities. In these cases, the Ritigrabe might respond earlier with a debris-flow initiation than the Dorfbach; however, the Ritigrabe curve is based on nine debris flows only, which might be insufficient for an accurate conclusion.

3.2 SedCas modelling This section presents the outcomes of the hydrological module of SedCas. First, the snow module was calibrated. The produced parameter values were further used to calibrate the complete hydrological module in terms of the AET. This way, more parameters, namely alpha, the parameter for the evapotranspiration efficiency, the residence time k and the soil water storage Vwcap, were calibrated with the aim to approach the best possible model performance. As the parameter calibration was carried out based on random parameter estimations and with the aim to find the best possible parameter combination for the hydrological module, it resulted in many different parameter combinations, which all reached equally accurate approximations of the observed AET. Hence, it should be considered that the chosen parameter set was just one of the best solutions. To test the reliability of the snow parameters, a validation was conducted. In this section, the different parameters along with their performance are presented. In the end, the hydrological module established the water balance of each debris-flow initiation area. Thus, the simulation results as well as the model performance are introduced.

3.2.1 Dorfbach, Randa The snow calibration of SedCas resulted in a slight underestimation of the simulated snow depths compared to the measured snow depths (Figure 12). This can have many different reasons, ranging from the fact that SedCas does not include snowdrift or the uncertainties of the precipitation data on measurement errors of the snow measuring station or that the station was not located exactly in the Dorfbach. Also, the constant snow water equivalent is a limitation and partly explains the underestimation. However, the seasonality is shown very well.

Figure 12 Simulated and observed snow depths for the Dorfbach

In the next step, the parameters of the hydrological module were calibrated. The parameter calibration for the Dorfbach resulted in a mean annual AET of 148 mm, whereas the determined AET was 147.73 mm. The simulations with SedCas provided insights to the annual water balance of the catchment (Figure 13). During May, precipitation and runoff were highest, whereas the maximum temperatures were detected in June to August.

Figure 13 Annual water balance for the Dorfbach based on interpolated data (P, T) and simulated data with SedCas (PET, AET, Q) As the hydrological module was based on the calibrated snow parameters, a validation was conducted to test the reliability of the used snow parameter set. The validation of the snow parameter set resulted in an annual absolute error of 6.83 mm between two different time periods. This value was considered to be low enough to allow for further calculations on the chosen snow parameter set (Table 3).

3.2.2 Ritigrabe, St. Niklaus For the Ritigrabe, the simulations of the SedCas model led to similar results as for the Dorfbach. Compared to the modeled data, the simulated snow depth was often underestimated but the seasonality was well-captured as shown in Figure 14.

Figure 14 Simulated and observed snow depth data for the Ritigrabe

With the chosen parameter combination, the hydrological module could reach an almost complete AET approximation (Table 4). The simulation produced an AET of 147 mm/y, whereas the observed AET was 147.73 mm/y. The simulated water balance presented the highest precipitation in May, when simultaneously a peak in the runoff was detected. However, runoff was much higher than precipitation, which can be explained by snowmelt due to rising temperatures (Figure 15). The validation of the hydrological module indicated that if the same snow parameter set is used for two different time periods, the annual absolute error of the simulated AET differs only 0.71 mm. For this reason, further calculations were all based on the previously determined snow parameter set (Table 4). This validation is proof that the snow parameter set is applicable to at least the Ritigrabe.

Figure 15 Annual water balance for the Ritigrabe based on interpolated data (P, T) and simulated data with SedCas (PET, AET, Q) In summary, the hydrological module of SedCas is strongly influenced by its stochastic character. The parameter estimation, which is based on random but uniformly distributed parameter values, led to several equally good approximations. However, just one parameter set was used for further simulations. This choice was random as well; thus, the simulations might be subject to large uncertainties.

3.3 Debris-flow trigger classification

This section presents the classification of the debris-flow triggers and shows the trigger mechanisms responsible for debris-flow initiation within each season. The studied trigger mechanisms were long-lasting rainfall events, short-duration storms, rain-on-snow events and intense snowmelt. The classification was established based on the simulations of SedCas, while the parameters were based on the period 48 hours before the occurrence of the debris flow. Hyetographs were studied for the debris-flow trigger classification. As an additional resource, percentiles of the hydrological variables were determined. The higher each parameter’s percentile was, the more likely a debris-flow initiation occurred because of this hydrometeorological feature.

3.3.1 Dorfbach, Randa During the study period, 34 debris flows were recorded in the Dorfbach. The debris-flow classification resulted in one debris flow initiated by intense snowmelt, six debris flows occurring in relation to short-duration storms and 12 debris flows triggered because of ongoing snowmelt that was intensified by rainfall, i.e. by so-called rain-on-snow events. Most of the debris flows were initiated by long-lasting rainfall events. These 15 debris flows occurred in June to October. Figure 16 illustrates a typical example, where a long-lasting rainfall was classified as the trigger mechanism. The triggering rainfall not only caused more runoff, but also filled the soil water storage until finally enough water was available to initiate a debris flow on 10/08/2015 in the early morning.

Figure 16 Debris flow on 10/08/2015 at 04:44 triggered by long-lasting rainfall Short-duration storms were responsible for six debris flows in the months of June to August. On the 14/06/2013, neither snowmelt nor a long-lasting rainfall event was detected. This debris flow was initiated by a short-duration storm at 17:45 in the evening (Figure 17). The rainfall caused an increase in the soil water storage and thus was sufficient to destabilize the loose debris in the torrent.

Figure 17 Debris flow on 14/06/2013 at 17:45 triggered by a short duration storm For 12 of the recorded debris flows, rain-on-snow events were responsible for the initiation. Snowmelt was either a process already occurring before the rainfall or a reaction to the rainfall. The rainfall events which could be related to snowmelt had different durations and intensities. Figure 18 presents the sequence of an intense rainfall event, which is followed by intense snowmelt and finally debris flow. For this debris-flow event, the exact initiation time is unknown. The possible event time is highlighted in grey.

Figure 18 Debris flow on 01/06/2009 triggered by a rain-on-snow event. Precise time of debris flow unknown, possible time (grey)

Intense snowmelt could only once be classified as a debris-flow trigger (Figure 19). On 28/05/2016, no significant triggering rainfall was observed. However, with increasing temperatures, the snow began to melt and thus contributed to a build-up in the soil water storage, which resulted in debris-flow initiation. For this debris flow, the exact event time is unknown. Figure 20 shows that the mean soil water storage (vw_m) before the debris flow initiation was fuller than 80% of the other events. Another striking parameter was the total snowmelt: almost 100% of the other events had lower values.

Figure 19 Debris flow on 28/05/2016 triggered by intense snowmelt, precise time of the debris flow unknown, possible time (grey)

Figure 20 Percentile ranges for the debris flow on the 28/05/2016, sm_g: snowmelt gradient, sm_t: total snowmelt, pet_g: gradient of pet, pet_t: total pet, p_g: precipitation gradient, p_t: total precipitation, p_max: maximum precipitation, fr: temporal fraction of precipitation, vw_g: water storage gradient, vw_m: mean water storage

3.3.2 Ritigrabe, St. Niklaus The debris-flow trigger classification for the nine studied debris flows in the Ritigrabe resulted in four debris flows which were initiated by long-lasting rainfall. Five debris flows could be traced back to rain-on-snow events. In the Ritigrabe, neither intense snowmelt nor short- duration storms could be identified as debris-flow triggers. Intense snowmelt can theoretically initiate debris flow; however, this only occurs in connection with specific rainfall events. Long-lasting rainfall typically filled the soil water storage until debris flow occurred. In the case of the debris flow on 25/07/1996, the soil water storage was already almost saturated (Figure 21). The long-lasting rainfall contributed the water still needed to trigger the debris flow. The exact time of the debris-flow initiation in the Ritigrabe is unknow, which is why a possible time span was plotted. It is assumed that the debris flow occurred when the soil water storage experienced a decrease because of the ongoing mass movement.

Figure 21 Debris flow on 25/07/1996 triggered by long-lasting rainfall, precise time of the debris flow unknown, possible time (grey) The majority of the debris-flow initiations were traced back to rain-on-snow events. In total, five of the nine studied debris flow in the Ritigrabe occurred because of simultaneous snowmelt and rainfall. At times, snowmelt acted either like a long-lasting rainfall event or a short-duration storm. Moreover, the sum of the snowmelt could be higher than the sum of the rainfall or the other way around. A typical snow-on-rain event can be seen in Figure 22 and 23. First, temperatures above the snowmelt threshold caused snowmelt. The soil water storage got filled up more and more by snowmelt and long-lasting rainfall, which led to the debris flow on the 25/09/1993.

Figure 22 Debris flow on 25/09/1993 triggered by rain-on-snow event, precise time of the debris flow unknown, possible time (grey): During these days, intense and long-lasting rainfall up to high elevations caused in the Canton of Valais hazardous floods and debris- flows occurred in many torrents also in the Matter valley (swisswetterforum 1993), also in the Dorfbach (Pralong et al., 2017)

Figure 23 Percentile ranges of the debris flow on 25/09/199, sm_g: snowmelt gradient, sm_t: total snowmelt, pet_g: gradient of pet, pet_t: total pet, p_g: precipitation gradient, p_t: total precipitation, p_max: maximum precipitation, fr: temporal fraction of precipitation, vw_g: water storage gradient, vw_m: mean water storage

The classification showed that during the study period, rain-on-snow events played an important role. Interestingly, the classification showed that debris flows which were triggered by rain-on-snow events occurred in May to September and not only in the main snowmelt period of May to July. Altogether, the debris-flow trigger classification indicated that long- lasting rainfall events, short-duration storms, intense snowmelt and rain-on-snow events were important debris-flow triggers. While in the Dorfbach all debris-flow triggers could be assigned to specific trigger mechanisms, the debris flows in the Ritigrabe were never triggered by short- duration storms or intense snowmelt only. In the Dorfbach, long-lasting rainfall events triggered the majority of the debris flows; in contrast, in the Ritigrabe rain-on-snow events led to most of the debris flows (Figure 24 & Table 6). Intense snowmelt without rainfall played a minor role. Generally, the monthly mean runoff was highest in April to June and the most debris flows occurred in July to August. Snow cover is supposed to inhibit some debris flows in spring (Bennett et al., 2014).

Figure 24 Comparison of the occurrence of debris-flow triggers in the Dorfbach and the Ritigrabe

Table 6 Debris-flows sorted by their trigger types long-lasting rainfall, short-duration storms, rain-on-snow events and intense snowmelt for the Dorfbach and the Ritigrabe

Long-lasting rainfall Short-duration storms Rain on snow events Intense snowmelt 16.06.1990 01.08.1990 01.06.1985 28.05.2016 02.06.1992 17.08.2012 20.06.1995 24.09.1993 14.06.2013 14.06.1996 20.07.1994 18.06.2013 15.07.2001 05.06.2002 12.06.2015 01.06.2009 15.07.2002 03.07.2015 31.05.2010 04.10.2005 04.06.2011 Dorfbach, Randa 07.06.2010 13.06.2013 14.08.2010 06.06.2014 03.06.2012 08.06.2014 02.07.2012 11.06.2014 29.07.2013 03.06.2016 28.07.2014 24.07.2015 10.08.2015 09.08.1991 25.08.1987 25.07.1996 25.09.1993 Ritigrabe, St. Niklaus 30.07.1996 25.09.1994 28.08.2002 30.05.2008 30.07.2014

4. Discussion

The initial objectives of this project were to identify the rainfall conditions which lead to debris- flow initiation and to study the hydrometeorological conditions before debris-flow initiation in the Dorfbach and the Ritigrabe. The previous chapter highlighted that in the debris-flow season from May to October, the debris flows were initiated by a wide range of rainfall intensities and durations. Beside rainfall, snowmelt had an important influence on debris-flow initiation in the study period of 01/10/1982 to 31/12/2017. The debris-flow trigger classification showed that rain-on-snow events were responsible for most of the debris flows (see overview Table 6). This chapter discusses the outcomes and gives suggestions for future research.

4.1 Uncertainties in input data As mentioned in chapter 2, meteorological data was obtained from the RhiresD product or from weather stations and further processed to approach the conditions in the Dorfbach and the Ritigrabe catchments. So, it should be considered that the data sets are likely uncertain. On one hand, the RhiresD data set has a daily resolution. The applied disaggregation, which is based on the comparison of precipitation measurements of close-by stations, is assumed to be reliable. Especially in mountainous areas, however, precipitation can occur spatially limited. Apart from the temporal resolution of the RhiresD data set, the spatial accuracy also is a critical point. RhiresD is an interpolated product which might be susceptible to underestimation of extreme events, and this in turn has a direct influence on the water balance and consequently the intensity-duration thresholds. The other meteorological input data sets are also subject to variable degrees of uncertainty due to the lack of weather stations in the study catchments as well as the different ways of data pre-processing, as described in the Methods section. It would have been interesting to have discharge data as an additional control of the ongoing processes. These are typical shortcomings in this field of research. Nevertheless, it was possible to estimate hydrological variables and the effect of their sub-daily variabilities on debris-flow triggering, which is exceptional.

4.2 Intensity-duration thresholds In Alpine catchments, rainfall is considered to be one of the most significant debris-flow triggers. This is confirmed by the present study of the debris-flow activity in the Dorfbach and the Ritigrabe. The results of this study indicate that in both catchments, debris flows can be generated by rainfall events that are characterized by a range of mean intensities of 0.1 to 6.3 mm/h and 0.1 to 6.6 mm/h, respectively. Triggering rainfall durations were 1 to 22 hours in the Dorfbach and 3 to 18 hours in the Ritigrabe. By studying the seasonal debris-flow triggering rainfall events, debris flows were observed to be initiated by various rainfall intensities and durations. It is noteworthy that debris flows in the Dorfbach during autumn were initiated by high to middle intensities and long-duration rainfall events. Surprisingly, the intensity-duration threshold plots showed no significant differences in the different seasons in terms of the debris flows in the Dorfbach or the Ritigrabe. The large spread occurred because snowmelt was not considered, the precipitation data was related to uncertainties and the precise time of the debris- flow initiation was unknown. As other results indicated, antecedent soil moisture or snowmelt too have an important influence on the debris-flow generation in the study region. Therefore, a possible explanation of the missing clear seasonal differences might be that snowmelt does not always occur with the same intensity during similar time periods. After a snow-poor winter or constant snowmelt over a long period of time, the torrent systems react differently than after a snow-rich winter or a sudden and high increase in temperature, which causes intense snowmelt. In case of the former, higher rainfall intensities are needed to trigger a debris flow and only a small additional water input can be sufficient to lead to a debris-flow initiation. Beside the

29 snowmelt contribution, the antecedent soil moisture also is variable depending on how the initiation area is currently covered (Bennett et al., 2014).

The comparison of the intensity-duration threshold curves presents a clear difference between the Dorfbach and the Ritigrabe. The curve of the Ritigrabe is much steeper and generally lower than the curve of the Dorfbach. Thus, in general rainfall events featuring the same duration require lower rainfall intensities to cause a debris flow in the Dorfbach than in the Ritigrabe. According to Pralong et al. (2017), the Dorfbach torrent bed is steeper (58%) in the upper section than the one of the Ritigrabe (43%), which might still change with every debris flow. However, the lower rainfall intensities in the Ritigrabe could also be related to different geological conditions and the availability of loose debris. Moreover, the Ritigrabe threshold is based on only a few debris-flow events, which might lead to an overinterpretation. In the Dorfbach, 34 debris flows were recorded, whilst in the Ritigrabe, only nine debris flows were included in the present study. If the catchments had experienced the same amount of debris flows during the study period, the results might have led to different intensity-duration thresholds.

Intensity-duration thresholds have limitations in their predictive power. The intensity-duration thresholds in this study were calibrated by the maximization of the True Skill Statistic. First of all, it is important to consider that this model performance is just one of several possible optimization criteria. Advantages of this approach are its prevalence independency and that it accounts for debris flow triggering and non-triggering rainfall events. Moreover, true and false alarms are included to symbolize important measures for early warnings. On the other hand, there is a large discrepancy between rainfall events which actually do trigger a debris flow and rainfall events which may lead to false alarms (Leonarduzzi et al., 2017). In general, intensity- duration threshold curves become more stable, the more debris-flow events are available for the computations (Peruccacci et al., 2017). However, if the obtained thresholds had been used for early warnings, this limitation would have been solved by different weighting of the sensitivity and specificity or, was applied in this study, by the removal of some low-intensity triggering events through setting a minimum intensity of 0.1 mm/h (Leonarduzzi et al., 2017).

Generally, intensity-duration thresholds are based on the assumption that rainfall is the only hydrological process controlling the generation of debris flows (Guzzetti et al., 2007). This may be true in humid and warm climates, but in cooler climates which are influenced by intense snowmelt in late spring and a build-up of soil moisture during summer, intensity-duration thresholds may be related to high uncertainties (Mostbauer et al., 2018). Furthermore, observations show that debris flows are often generated not by precipitation itself but due to the water which percolates into the ground and causes further failure (Caine 1980, Guzzetti et al., 2008). In consideration of these decisive factors, intensity-duration thresholds cannot be conclusively limited to rain-dominated catchments.

All in all, the analysis of the intensity-duration thresholds gave first insights to the debris-flow generation in the Dorfbach and the Ritigrabe. However, this method comes along with the above-described limitations out of which the limitation of only considering rainfall is seen as one of the largest weaknesses.

4.3 SedCas – the hydrological module The concept of the intensity-duration threshold model is well-established and applied in many studies (Guzzetti et al 2007 & 2008; Leonarduzzi et al., 2017 and more). Still, intensity-duration thresholds focus only on rainfall. SedCas simulates debris flows as a function of several contributing hydrometeorological factors (Bennett et al., 2014). The hydrological module of SedCas was calibrated with consistent meteorological data sets, which were pre-processed with additional information. For the calibration, unknown parameters were randomly sampled, and combinations were tested to achieve the best possible model performance. On one hand, the iterative approach of the parameter calibration allows for the testing of many possibilities. On the other hand, this approach is likely to result in several best parameter sets. For this study, one best parameter set was chosen. However, if in further analyses all best parameter sets should be included to quantify uncertainties. Despite uncertainties, the simulations for this study provided complete water balances of the Dorfbach and the Ritigrabe, as SedCas includes all major processes relevant to the water balance. Since it is a lumped conceptual model, it provides estimates of the hydrological processes. Another advantage is the temporal resolution. The simulations were conducted at an hourly rate, which allows for detailed insights to the hydrological state before debris-flow occurrence. The spatial resolution, however, is a limitation. The model is lumped and sees the catchments as spatially averaged. This means that small-scale processes, like local snowmelt on hills and in depressions, snowdrift or avalanches, which are all of interest as they may inhibit erosion or increase the debris-flow susceptibly due to higher base flow, cannot be captured in the model (Bennett et al., 2014).

Although the hydrological module of SedCas provides all important hydrometeorological processes which can facilitate debris flows, it would have been interesting to use the sediment module as well. First of all, SedCas can provide more information on debris-flow generation if both modules are used. Secondly, debris flows are defined as torrential flows which constitute of a mixture of water, debris and mud (Takahashi, 2014). Therefore, it is essential to study not only the hydrological processes, but also the geomorphological conditions that lead to debris flows. Because the needed magnitude-frequency analysis of hillslope failures filling the channel with sediments and detailed information on the erodibility of the debris-flow initiation areas are missing in the study catchment, the sediment module could not be included. Moreover, the model would have to be further developed for the catchments of interest, e.g. by developing a way to include rock glaciers, but due to a lack of time, this has to be the subject of future research. Using the complete SedCas model, one could investigate future changes of the debris- flow behaviour in terms of magnitudes and frequencies in consideration of different climate change scenarios in the Dorfbach and the Ritigrabe.

4.4 Debris-flow trigger classification The debris-flow generation and evolution are highly influenced by local geomorphological conditions such as slope aspect and the erodibility in the catchment (Prenner et al., 2018a). Apart from these conditions, debris flows are dependent on the availability of sufficient water to support the sediment transport. Therefore, meteorological conditions and their impact on debris-flow initiation are studied in more detail. Based on the simulations of SedCas, debris flows in the Dorfbach and the Ritigrabe were grouped into debris-flow trigger classes. The determined debris-flow trigger classes were long-lasting rainfall (LLR), short-duration storms (SDS), rain-on-snow events (ROS) and intense snowmelt (SM). The debris-flow trigger classification was conducted by the analysis of hydrographs and the study of percentile ranges of different meteorological and hydrological parameters. The classification was conducted qualitatively, and no definite thresholds were determined. Uncertainties in the classification process are likely if indications are only weakly pronounced. The classification was especially critical for short-duration storms as the RhiresD data cannot

31 capture convective storms. The model is lumped, so the trigger type intense snowmelt only is also challenging to classify. Moreover, uncertainties can originate from limitation of the trigger classes to just four types. With the study of the hydrometeorological conditions 48 hours before the debris-flow occurrence, just the most immediate processes can be captured. Nevertheless, cascades of hydrological processes can also lead to debris-flow initiation over a longer time span. Even tough percentiles of the soil water storage were considered, it is difficult to explain the causes in the long run. It may be the case that during more than two days before the debris flow, intense snowmelt occurred followed by a long-lasting rainfall event. However, a short- duration storm added enough water to initiate a debris flow and consequently, this event was classified as short-duration storm. By only focusing on 48 hours before the debris flow, the other factors possibly responsible for a strong soil moisture build-up cannot be explained in detail. Still, as the catchments are steep and lack vegetation, residence times can be assumed to be short. This, in turn, supports the decision of focusing on only 48 hours.

This study highlights that debris flows in the Dorfbach and the Ritigrabe can be initiated by a wide range of hydrometeorological processes. Even tough long-lasting rainfall and rain-on- snow events were the debris-flow triggers occurring the most, no single trigger type can ultimately be determined to be the most significant one. Furthermore, the various rainfall conditions which are needed to trigger a debris flow can be explained by the variation in the soil water storage before the debris flow. This, in turn, represents a clear argument for including more hydrological parameters instead of establishing intensity-duration thresholds which depend only on rainfall data.

Future studies could conduct the debris-flow trigger classification quantitatively to determine exact thresholds for more powerful conclusions. Moreover, the classification could be validated with information from weather reports of MeteoSwiss. With the study of several catchments in the Matter valley or in the European Alps, one could contribute to a better understanding of the hydrometeorological conditions which favour debris-flow triggering.

4.5 The role of rock glaciers as sediment source Thus far, the study analysed and discussed hydrometeorological processes that lead to the initiation of debris flows. As pointed out in different sections, it is highly important to learn more about the hydrological state before the debris-flow initiation. Nevertheless, a debris flow consists of two major contributions: one is water, the other one is debris. This section focuses on geomorphological aspects which may contribute to the sediment load transported in the Dorfbach and in the Ritigrabe. Even though for the use of the SedCas sediment module information is missing, there is knowledge on debris sources in the study regions. Both the Dorfbach and the Ritigrabe are situated in a periglacial environment where permafrost thaw and rock glaciers play an important role (Pralong et al., 2017). Rock glaciers are characterized by ice-supersaturated debris or pure ice, which is covered by an active layer, a thick, seasonally frozen debris layer (Jones et al., 2019). Especially the rock glaciers are expected to largely contribute to the studied debris flows.

4.5.1 Dorfbach, Randa Material inputs in the Dorfbach mainly originate from the erosion front of the Grabengufer rock glacier, which is located right above the debris-flow initiation area (Pralong et al., 2017). The tongue of the Grabengufer rock glacier ends at about 2400 m a.s.l. on a very steep slope section, where rapid erosion was observed (Graf, 2013). During the study period, different displacement rates were detected. The current crisis is supposed to have started between 1982 and 1988 and thus the beginning of the study period of this thesis. The rooting zone of the rock glacier suffered progressive overloading due to rockfalls and landslide activity in the head areas, which led to a compressive wave that started from the rooting zone. The acceleration of the frontal part of up to 5 m/year increased between 2001 and 2005 until the complete destabilization between July 2009 and February 2010, when exceptionally high displacement rates of 10 to 40 cm/day were recorded (Delaloye et al., 2013). In 2013, it was estimated that 70’000 to 120’000 m3 of loose material was displaced into the debris-flow initiation area of the Dorfbach and an additional 8’000 m3 of loose debris are released every year into the debris flow initiation area (Graf, 2013).

4.5.2 Ritigrabe, St. Niklaus The Ritigrabe torrent is also mainly fed by debris originating from a rock glacier, which is located in the upper part of the debris-flow initiation area. Here, the front of the ice-rich rock glacier terminates at around 2600 m a.s.l. (Kenner et al., 2018). The rock glacier shows increasing movement rates since the early 1990s. At a decadal scale, changes in the rock glacier creep rate go along with changes of the air temperatures in the European Alps (Delaloye et al., 2013). Compared to the Grabengufer rock glacier, the Ritigrabe rock glacier showed slower surface velocities of 0.6 to 0.9 m/y in 1995, leading to 300 – 400 m3 sediment delivery per year. Over the last decades, however, the surface velocity increased by over 70%. This results in larger sediment release rates at the rock glacier front (Lugon & Stoffel, 2010). Studies (e.g. Kenner et al., 2017) show that the Ritigrabe rock glacier is strongly influenced by water fluxes. The rock glacier creep rate experiences a rapid acceleration during snowmelt or rainfall due to lower frictional resistance in the shear zones. This observation may also be an explanation for the widespread rock glacier acceleration in the Alps (Kenner et al., 2017). In the Ritigrabe, the debris-flow frequency decreased during the last years, which increases the potential for very large debris-flow events after high-intensity rainfall in the future (Lugon & Stoffel, 2010).

Fast-moving rock glaciers deliver significant quantities of debris to downstream gullies and channels. This may lead to a change in the debris-flow activity and consequently may increase the risk of damage in the catchment (Delaloye et al., 2013). Rock glacier movements are controlled by the kinematical behaviour and by weather conditions (Kummert et al., 2017). Permafrost thaw facilitates new ways for water fluxes (Kenner et al., 2017). Observations show that not only different annual creep rates but also interannual variations can be determined. In the Western Swiss Alps, minimum velocities occur in April to May and accelerated movements followed mainly in the period of snowmelt. The velocities of the Grabengufer rock glacier increased until November (Delaloye & Staub, 2016). However, in the course of global warming, the Alps are confronted with rising temperatures and more high- intensity rainfall events (NCCS, 2018). Consequently, rock glacier creep rates are expected to increase, which facilitates sediment transfer between rock glaciers and torrents. If significant quantities of debris get displaced in debris-flow initiation areas, the risk of more hazardous debris flows may increase. Furthermore, permafrost thaw can increase the occurrence of rockfalls and landslides, what increases the sediment input into the debris-flow initiation areas (Kummert et al., 2017).

5. Conclusions

This thesis aimed to investigate the questions of which hydrological state before the debris- flow initiation was present, and which hydrometeorological processes were responsible for the debris-flow initiation in two Alpine catchments. Both the Dorfbach and the Ritigrabe catchments are located in the Matter valley and experience regular debris flows of various magnitudes.

First, intensity-duration thresholds were established, and they showed that different rainfall intensities and durations may initiate a debris flow, especially during spring and summer. Debris flows in autumn often required long and intense rainfall. In the Ritigrabe, the debris- flow initiation generally depended on higher rainfall intensities at equal rainfall durations than in the Dorfbach. Since debris flows can be triggered not only by rainfall but also by other hydrological processes such as increasing soil moisture and snowmelt, the hydrological module of SedCas was used to simulate the entire water balance of the torrents. Based on these simulations, a qualitative debris-flow trigger classification was established. In the Dorfbach, most of the debris flows were triggered by long-lasting rainfall and by rain-on-snow events. Some were triggered by short-duration storms. Intense snowmelt triggered one debris flow out of the 34 events in total. In the Ritigrabe, the nine debris flows were triggered either by rain- on-snow events or by long-lasting rainfall. So, during the study period of 01/10/1983 to 31/12/2017, the most important debris-flow triggers were long-lasting rainfall and rain-on- snow events.

Debris flows are triggered by an interplay of hydrological and geomorphological processes. Both catchments are fed by the voluminous erosion of rock glaciers. In the course of climate change, permafrost thaw is expected to increase, which results in more destabilized slopes and rock walls and thus can contribute to higher sediment inputs into the debris-flow initiation areas. Furthermore, high-intensity rainfall events are projected to occur more often in the future, even in higher elevations. Together, the potential for hazardous debris flows is expected to increase. A regional debris-flow warning requires the quantification of hydrometeorological trigger mechanisms which have a decisive impact on debris-flow initiation. At the same time, the understanding of the long-term behaviour of the periglacial environments is essential and the release of sediments has to be quantified in order to be able to reliably asses the actual risk.

An extension of this thesis by the use of the complete SedCas model may shed light on clearer patterns in the debris-flow activity in the Matter valley. Additionally, the inclusion of climate scenarios may give insights into the future behaviour of the torrents. Future research is further essential to increase the applicability of the SedCas model to other catchments to allow for a more in-depth study. Moreover, the quantification of the decisive parameters for the debris- flow initiation and the automatization of the classification procedure can help to approach the overall aim of a better debris-flow prediction.

6. Acknowledgments

This section is dedicated to all the people who supported my work in one or the other way. First of all, I want to thank Jacob Hirschberg, my supervisor at WSL. The accomplishment of my thesis would not have been possible without his great support. Thanks as well to Andrew Frampton, my supervisor from Stockholm University, who let me work very independently. Since the beginning of my work at WSL, I appreciated that Brian McArdell shared his great knowledge on debris flows with me and offered constructive feedback towards the end of the writing process. I thank Christoph Graf for providing geophone data and Luisa Pruessner for sharing snow data from the IMIS station. A special thank goes to Reynald Delaloye for an inspiring meeting at the University of Fribourg and for sharing webcam images from the Grabengufer rock glacier as well as some unpublished reports about his group's current work. For the final improvements, especially in terms of language, I want to thank Karin MacKevett. I am very grateful for all the input from team members of WSL's research unit “Mountain Hydrology and Mass Movements” after my presentation at the institute and for making me feel welcome from the first day on. Lastly, I want to thank the Federal Institute for Forest, Snow and Landscape Research (WSL) for hosting my thesis, providing data and technical support and for giving me the chance to work in such an inspiring and encouraging environment.

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Appendix

Table 7 Monthly temperature lapse rate

lapse rate month [°C/100m] 1 -0.31 2 -0.39 3 -0.52 4 -0.62 5 -0.62 6 -0.61 7 -0.57 8 -0.53 9 -0.49 10 -0.4 11 -0.36 12 -0.3

Table 8 Debris flow events times in the Ritigrabe

day month year Start time end time 1 1 1922 00:00 00:00 4 9 1948 00:00 00:00 23 9 1953 00:00 00:00 24 9 1953 00:00 00:00 29 8 1977 00:00 00:00 30 8 1977 00:00 00:00 2 6 1962 00:00 00:00 24 8 1987 00:00 00:00 8 8 1991 00:00 00:00 24 9 1993 00:00 00:00 24 9 1994 00:00 00:00 24 7 1996 00:00 00:00 29 7 1996 00:00 00:00 27 8 2002 00:00 00:00 29 5 2008 00:00 00:00 29 7 2014 00:00 00:00

Table 9 Debris flows event time in the Dorfbach

day month year start time end time 1 8 1899 00:00 00:00 1 1 1923 00:00 00:00 1 1 1935 00:00 00:00 1 1 1945 00:00 00:00 1 1 1967 00:00 00:00 1 1 1962 00:00 00:00 30 6 1963 00:00 00:00 1 6 1985 00:00 00:00 1 8 1990 00:00 00:00 16 6 1991 00:00 00:00 2 6 1992 00:00 00:00 24 9 1993 00:00 00:00 20 7 1994 00:00 00:00 20 6 1995 00:00 00:00 14 6 1996 00:00 00:00 15 7 2001 00:00 00:00 5 6 2002 00:00 00:00 15 7 2002 00:00 00:00 4 10 2005 00:00 00:00 1 6 2009 00:00 00:00 31 5 2010 00:00 00:00 7 6 2010 00:00 00:00 14 8 2010 00:00 00:00 4 6 2011 02:54 03:40 3 6 2012 20:05 21:55 2 7 2012 04:13 18:14 17 8 2012 16:57 16:57:52 13 6 2013 17:28 21:31 14 6 2013 18:45 20:15 18 6 2013 17:29 18:53 29 7 2013 08:30 16:41 6 6 2014 19:22 19:44 8 6 2014 17:49 18:11 11 6 2014 19:49 20:56 29 7 2014 00:33 05:00 12 6 2015 10:55 12:34 3 7 2015 20:45 02:40 24 7 2015 17:57 11:01 10 8 2015 05:44 09:41 28 5 2016 00:00 00:00 3 6 2016 00:00 00:00

Dorfbach – Plots used for the trigger type classification

Figure 25 Dorfbach: Trigger type: intense snowmelt, in grey possible event time, Q is discharge, Qs is surface runoff, Qmelt is runoff by snowmelt, T is temperature, Vw is soil water storage

Figure 26 Dorfbach: Trigger type: long-lasting rainfall

Figure 27 Dorfbach: Trigger type: long-lasting rainfall

Figure 28 Dorfbach: Trigger type: Short-duration storm

Figure 29 Dorfbach: Trigger type: short-duration storm

Figure 30 Dorfbach: Trigger type: long-lasting rainfall

Figure 31 Dorfbach: Trigger type: rain-on-snow event

Figure 32 Dorfbach: Trigger type: rain-on-snow event

Figure 33 Dorfbach: Trigger type: rain-on-snow event

Figure 34 Dorfbach: Trigger type: long-lasting rainfall

Figure 35 Dorfbach: Trigger type: short-duration storm

Figure 36 Dorfbach: Trigger type: short-duration storm

Figure 37 Dorfbach: Trigger type: rain-on-snow event

Figure 38 Dorfbach: Trigger type: short-duration storm

Figure 39 Dorfbach: Trigger type: long-lasting rainfall

Figure 40 Dorfbach: Trigger type: long-lasting rainfall

Figure 41 Dorfbach: Trigger type: rain-on-snow event

Figure 42 Dorfbach: Trigger type: long-lasting rainfall

Figure 43 Dorfbach: Trigger type: long-lasting rainfall

Figure 44 Dorfbach: Trigger type: rain-on-snow event

Figure 45 Dorfbach: Trigger type: rain-on-snow event

Figure 46 Dorfbach: Trigger type: long-lasting rainfall

Figure 47 Dorfbach: Trigger type: long-lasting rainfall

Figure 48 Dorfbach: Trigger type: long-lasting rainfall

Figure 49 Dorfbach: Trigger type: rain-on-snow event

Figure 50 Dorfbach: Trigger type: rain-on-snow event

Figure 51 Dorfbach: Trigger type: rain-on-snow event

Figure 52 Dorfbach: Trigger type: long-lasting rainfall

Figure 53 Dorfbach: Trigger type: long-lasting rainfall

Figure 54 Dorfbach: Trigger type: long-lasting rainfall

Figure 55 Dorfbach: Trigger type: long-lasting rainfall

Figure 56 Dorfbach: Trigger type: short-duration storm

Figure 57 Dorfbach: Trigger type: rain-on-snow event

Figure 58 Dorfbach: Trigger type: rain-on-snow event

Ritigrabe: Plots for the debris-flow trigger type classification

Figure 59 Ritigrabe: Trigger type: rain-on-snow event

Figure 60 Ritigrabe: Trigger type: long-lasting rainfall

Figure 61 Ritigrabe: Trigger type: long-lasting rainfall

Figure 62 Ritigrabe: Trigger type: long-lasting rainfall

Figure 63 Ritigrabe: Trigger type: rain-on-snow event

Figure 64 Ritigrabe: Trigger type: rain-on-snow event

Figure 65 Ritigrabe: Trigger type: long-lasting rainfall

Figure 66 Ritigrabe: Trigger type: rain-on-snow event

Figure 67 Ritigrabe: Trigger type: rain-on-snow event