Dynamics and Flow Behavior of Natural Debris Flows: From Velocity Profiling to Rheological Insights

Supervisor: Roland KAITNA

Project assigned to: Maximilian ENDER

Introduction

Debris flows can be defined as highly variable concentrated sediment-water mass flows (Hungr et al., 2014; Iverson, 1997; Takahashi, 2007), whereby the solid volume fraction can easily reach values up to 40 to 90 % (Lavigne and Suwa, 2004). The main trigger mechanisms are predominantly short-duration convective precipitation events (Corominas et al., 1996; Guzzetti et al., 2008; Nikolopoulos et al., 2017; Wieczorek and Glade, 2005), although snowmelt (Decaulne et al., 2005; Mostbauer et al., 2018), dam break effects (Capart et al., 2001; Cui et al., 2010; Fang et al., 2019; Sattar et al., 2022) or landslides (Gabet and Mudd, 2006; Iverson et al., 1997; Sassa and Wang, 2005) can also play a role as initiation mechanisms. The process follows generally a steep channel (“torrent” or “creek”) and often consists of a variable number of surges which can obtain high velocities approaching 10 ms^-1 (Nagl et al., 2020). This leads to highly destructive power that endangers settlements, infrastructure and human lives (Castelli et al., 2023; Dowling and Santi, 2014; Haque et al., 2019; Schlögl et al., 2021). Sediment grain sizes can include a wide range of magnitudes (Kaitna et al., 2014; Rickenmann, 2016), are often subject to a wide dispersion and can vary greatly within a single event or during different debris-flow events in the same catchment (Arattano and Franzi, 2004; Hübl, 2018; Marchi and Cavalli, 2007; Rickenmann, 1997; Theule et al., 2012). The flow behavior is strongly dependent on the respective grain size distribution of the mixture (Iverson et al., 2010). While the front of a typical debris flow usually consists of coarse particles, the body is made up of more liquid components, typically with smaller grain sizes in suspension (Coussot and Meunier, 1996; Hungr, 2005; Zhou and Ng, 2010). 

A key factor in investigating the flow behavior of debris flows is the consideration of flow resistance. One fundamental approach for assessing flow resistance, besides modelling, involves examining velocity distributions, as both mean velocities and velocity fluctuations provide insights into the bulk flow dynamics of the mixture (Du et al., 2021; Kaitna et al., 2014; Lanzoni et al., 2017; Larcher et al., 2007). Since velocity plays a decisive role in the erosion, transport, and deposition behavior of debris flows, estimating vertical velocity distributions provides a robust basis for analyzing internal deformation processes and conducting subsequent rheological analyses.

The focus of this PhD thesis is to derive vertical velocity profiles in natural debris flows, investigate the dynamics within and between different debris flows and carry out rheological interpretations of the observed flow behavior. This leads to the following research questions:

1. How can the accuracy and reliability of deriving vertical velocity profiles in natural debris flows be optimized with respect to parameter sensitivity, measurement system performance, and achievable temporal resolution?
2. How can flow behavior in debris flows be described based on observed vertical velocity profiles, and how do these profiles relate to material properties and laboratory observations?
3. Can consistent patterns in vertical velocity profiles, wave dynamics, and stress conditions be identified across different natural debris flows, providing a basis for rheological interpretations? 

Methods

(a) Study area and monitoring station

The catchment of Gadria creek is located in the upper Vinschgau/Venosta valley in the Autonomous Province of Bolzano – South Tyrol, Italy. The Gadria-Strimm creek system has formed an exceptionally large fan (megafan), which covers an area of over 10 km² and has had a significant impact on the postglacial landscape evolution of the glacially shaped main valley (Comiti et al., 2014; Jarman et al., 2011). The catchment area lies south of the Alpine ridge, within the Oetztal Unit, a subrange of the Central Eastern Alps. The interplay of large volume of fragmented bedrock, glacial deposits, and high relief energy together with convective precipitation events between June and September (c.f. Della Chiesa et al., 2014) leads to the regular occurrence of debris flows with an annual frequency of approximately 1 to 2 events (Cavalli et al., 2013). Climatologically, the field site is located within a pronounced alpine dry region, resulting from the central Alpine position of the upper Vinschgau/Venosta valley and the associated orographic rain shadow effects (Dell’Agnese et al., 2015), with annual precipitation amounts below 600 mm in the valley ground (Brugnara et al., 2012).

The monitoring station is situated at the fan apex at 1,500 m a.s.l. It consists, on the one hand of a concrete barrier, located in the middle of the channel bed, constructed in 2017. On the other hand, a meteorological station is installed on the orographic left channel bank, and an iron bridge spans the channel upstream of the monitoring barrier. The monitoring installations enable temporal high-resolution measurements of debris-flow parameters, including flow depth, impact forces, normal and shear stresses, and pore fluid pressure. The full details of the monitoring station setup including a description of all measured parameters can be found in (Nagl et al., 2020).

(b) Derivation of vertical velocity distributions

The use of cross-correlation to derive flow velocities from sensor signals is grounded in a broad range of experimental studies (e.g., Ahn et al., 1991; Boateng and Barr, 1997; Bowman and Take, 2015; Dent et al., 1998; Kaitna et al., 2014; McElwaine and Tiefenbacher, 2003; Schaefer et al., 2010; Schaefer and Bugnion, 2013; Sovilla et al., 2014; Tiefenbacher and Kern, 2004; Wei et al., 2012), all of which share the fundamental principle of deriving a time lag t between the two signals. Since the spatial offset between the signal sensors is known, velocities can be determined via the relationship v = s/t. 

In natural debris flows, the method of cross-correlation time-shifted sensor signals, has been applied using seismic and ultrasonic sensors (Arattano and Marchi, 2005). Nagl et al., 2020 successfully transferred the technique of correlating paired conductivity sensors from laboratory setups (Kaitna et al., 2014) to field measurements and demonstrated that vertical velocity profiles can be derived in natural debris flows at the Gadria monitoring station. To resolve the temporal evolution of velocity, a floating time window is applied to the conductivity signal data. For each window, a cross-correlation is performed across all sensor levels of the velocimeter bar, yielding a potential representative velocity for each level and window. Velocity estimates are obtained for sensor levels contacted by the passing debris-flow mass within the respective time window. This approach provides a time series of velocity values rather than a single average over the entire measurement period. 

Preliminary results 

(a) Parameter sensitivity analysis 

As key parameters for a robust cross-correlation-based derivation of velocity distributions of natural debris flows at the Gadria creek, the length of the floating window, the overlap of the floating window, a cutoff value for the ACF (correlation coefficient), and a cutoff interval for the correlated velocities (to exclude unrealistic velocity estimates) are defined and subjected to a sensitivity analysis. For this purpose, a distinction is made between “steady” flow segments, characterized by minimal changes in material composition and flow depth, and “unsteady” flow segments, which exhibit, strong, temporal short-term variations in material composition and flow depth (e.g., during waves). 

The results indicate that the ACF cutoff and the length of the floating window exert the strongest influence on the derived velocity distributions, while the overlap interval and the velocity interval play a secondary role. Additionally, there exists a lower-bound velocity that cannot be undershot. This limit arises from the constraints imposed by the cross-correlation method, the measurement frequency, and the sensor spacing. It is primarily dependent on the floating window length and increases as the window length decreases

(b) Basal slip velocities

A similar sensor setup was installed at the Lattenbach creek in Tyrol, Austria, on the channel bed with the aim of obtaining more detailed information on basal slip velocities in natural debris flows. Some laboratory experiments indicate the presence of basal sliding in debris flows (e.g., Sanvitale and Bowman, 2010; Taylor-Noonan et al., 2022), whereas most simple shear models assume a no-slip condition (e.g., Berzi and Jenkins, 2008; Garres-Díaz et al., 2020; Luna et al., 2012; Pastor et al., 2021; Pudasaini, 2012). Initial analyses of sensor data for two recent debris-flow events reveal the continuous presence of a basal slip velocity.

For more details, see: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4872/. 

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