Debris flows represent frequent hazards in mountain regions. Though significant effort has been made to predict such events, the trigger conditions as well as the hydrologic disposition of a watershed at the time of debris flow occurrence are not well understood. Traditional intensity-duration threshold techniques to establish trigger conditions generally do not account for distinct influences of rainfall, snowmelt, and antecedent moisture. To improve our knowledge on the connection between debris flow initiation and the hydrologic system at a regional scale, this study explores the use of a semi-distributed conceptual rainfall–runoff model, linking different system variables such as soil moisture, snowmelt, or runoff with documented debris flow events in the inner Pitztal watershed, Austria. The model was run on a daily basis between 1953 and 2012. Analysing a range of modelled system state and flux variables at days on which debris flows occurred, three distinct dominant trigger mechanisms could be clearly identified. While the results suggest that for 68 % (17 out of 25) of the observed debris flow events during the study period high-intensity rainfall was the dominant trigger, snowmelt was identified as the dominant trigger for 24 % (6 out of 25) of the observed debris flow events. In addition, 8 % (2 out of 25) of the debris flow events could be attributed to the combined effects of low-intensity, long-lasting rainfall and transient storage of this water, causing elevated antecedent soil moisture conditions. The results also suggest a relatively clear temporal separation between the distinct trigger mechanisms, with high-intensity rainfall as a trigger being limited to mid- and late summer. The dominant trigger in late spring/early summer is snowmelt. Based on the discrimination between different modelled system states and fluxes and, more specifically, their temporally varying importance relative to each other, this exploratory study demonstrates that already the use of a relatively simple hydrological model can prove useful to gain some more insight into the importance of distinct debris flow trigger mechanisms. This highlights in particular the relevance of snowmelt contributions and the switch between mechanisms during early to mid-summer in snow-dominated systems.
Debris flows are rapidly flowing mixtures of sediment and water transiting steep channels (Hungr et al., 2014) and often represent a severe hazard in mountain regions. In Alpine regions the mechanism of debris flow initiation typically ranges from distinct slope failures transforming into a flow-like movement to intensive sediment bulking due to channel erosion (e.g. Rickenmann and Zimmermann, 1993; Prancevic et al., 2014). Hereafter we refer to debris flows as channel-based mass flows that can be triggered by either landsliding or channel erosion. In contrast to the effect of a region's geomorphological and geological disposition to debris flows (e.g. Nandi and Shakoor, 2008; von Ruette et al., 2011) and in spite of significant efforts in the past (e.g. Guzzetti et al., 2008), neither the effect of hydrologic disposition (i.e. the general wetness state) of a specific region at the time of debris flow initiation nor the actual triggering hydro-meteorological conditions are well understood. Reliable regional predictions of debris flow events so far therefore remain essentially elusive.
There is a widespread consensus that high-intensity, short-duration rainfall is the primary trigger of debris flows in Alpine environments (e.g. Berti et al., 1999; Marchi et al., 2002; McArdell et al., 2007; McCoy et al., 2012; Kean et al., 2013), while longer-duration precipitation is of minor but not negligible importance (e.g. Moser and Hohensinn, 1983; Stoffel et al., 2011). However, little is known about the influence of other factors such as snowmelt or the antecedent soil moisture, which may increase a catchment's susceptibility to debris flow initiation by reducing the additional water input needed to trigger a debris flow (“the disposition concept”; Kienholz, 1995).
While antecedent wetness, quantified as pre-storm rainfall, has been widely observed as an important factor for triggering debris flows (e.g. Napolitano et al., 2016), there is little agreement on the specific water volumes and/or time periods required for the build-up of debris flow-relevant antecedent soil moisture (Wieczorek and Glade, 2005). Similarly, there is no consensus on the level of soil moisture, i.e. the water volume stored in near-surface layers of the unsaturated substrate, required to trigger debris flows under different rainfall conditions (Johnson and Sitar, 1990; Montgomery et al., 2009). Essentially omitting the temporally variable yet cumulative influences of evaporation, transpiration and drainage on the soil wetness state, these concepts of antecedent wetness should be treated with caution and may hold only limited information. Interestingly, Aleotti (2004) and Berti et al. (2012) found no significant influence of antecedent rainfall, as a proxy for soil moisture, on the triggering of landslides and debris flows in different regions in Italy. This is somewhat surprising, as slope failures are to be expected to occur more readily under situations with elevated pore fluid pressures (Iverson, 2000). Such somewhat contrasting interpretations probably arose from slightly different definitions of antecedent rainfall, which mask what is effectively the role of soil moisture (see discussion in Berti et al., 2012). In the specific cases where the triggering rainfall was restricted to the rainfall on the event day (e.g. Glade et al., 2000), the role of antecedent rainfall was interpreted to be higher than in cases where the definition of events was widened to longer durations (e.g. Berti et al., 2012). However, other research has identified catchments where the antecedent wetness does not have substantial impact on the triggering of different types of mass movements, including landslides and debris flows (Deganutti et al., 2000; Coe et al., 2008; Ciavolella et al., 2016; Chitu et al., 2017).
Similarly, snowmelt, often combined with rainfall (“rain-on-snow”), is recognized as a common triggering factor of debris flows (Church and Miles, 1987) and shallow landslides (which may subsequently transform into debris flows) (Bíl et al., 2015). In spite of this general understanding, there is little systematic effort to quantify its influence, and its role may often be underestimated (Decaulne et al., 2005).
Detailed, direct observations of these two (e.g. Johnson and Sitar, 1990; Coe et al., 2008; Montgomery et al., 2009) and other potentially relevant system components, such as canopy interception (e.g. Sidle and Ziegler, 2017), are typically not available at sufficient spatial and temporal resolutions. This is in particular true for debris flow-prone, mountainous environments, and if measurements are available, they are mostly limited to point observations in small, experimental catchments over relatively short time periods, including, if any, only a few debris flow events. Notwithstanding these limitations, estimates of spatial distributions of soil water storage from relatively low-resolution observations or at least relative differences in its spatial occurrence are often used for the identification of locations more susceptible to mass movements, including shallow landslides, and less often, debris flows, than others in regional hazard assessments (cf. Bogaard and Greco, 2016).
Besides liquid water input and subsurface water storage a region's susceptibility to debris flows is also strongly influenced by its landscape and the past evolution thereof (Takahashi, 1981; Rickenmann and Zimmermann, 1993; Reichenbach et al., 2014; Sidle and Ziegler, 2017). More specifically, the type of underlying bedrock and its resistance to weathering are, together with the associated soil formation/erosion processes (i.e. sediment availability), vegetation cover (i.e. reduction of effective rainfall intensities and “reinforcement” of soil) in constant feedback with the resulting topography (i.e. gradient), another first-order control on debris flows.
Since the pioneering work of Montgomery and Dietrich (1994), considerable progress has been made in understanding and describing the interplay between the above hydrological and geomorphological/geological susceptibility of hillslopes and small catchments to mass movements based on elegant, spatially explicit, high resolution mechanistic model frameworks (e.g. Dhakal and Sidle, 2004; Simoni et al., 2008; Lehmann and Or, 2012; Mancarella et al., 2012; von Ruette et al., 2013; Anagnostopoulos et al., 2015). Despite their outstanding value for developing our understanding of the detailed processes and feedbacks involved in the initiation of mass movement events as well as for local predictions of such (mainly shallow landslides) at the study sites, these models have at the present and for the foreseeable future limited value for larger-scale applications (cf. Hrachowitz and Clark, 2017). In order for being meaningful descriptions of reality, they need to rely on detailed descriptions of the spatial and temporal natural heterogeneity of both the meteorological conditions and the subsurface. For example, Fan et al. (2016) demonstrated that spatial variations in soil properties, without changing other boundary conditions, lead to considerable variations in landslide occurrence characteristics. While ever-improving remote sensing products continue to alleviate the problems of the availability of suitable meteorological data, a meaningful and detailed characterization of the multi-scale subsurface heterogeneity is out of reach for the vast majority of regions worldwide. Without this information, though, such models cannot be adequately calibrated (i.e. equifinality; Beven, 2006a) or rigorously tested (i.e. the boundary flux problem; Beven, 2006a), making them problematic to use as debris flow prediction tools at the spatial scales and extent of relevance for operational early-warning systems.
In contrast, efforts to provide meaningful and feasible debris flow prediction tools are largely limited to statistical model frameworks with little explicit consideration of the physical processes involved (e.g. Baum and Godt, 2010; Papa et al., 2013; Berenguer et al., 2015). The vast majority of these applications rely exclusively on the well-established concept of intensity-duration thresholds (e.g. Aleotti, 2004; Guzzetti et al., 2007, 2008 and references therein), or apply other probabilistic assessments of rainfall characteristics (Berti et al., 2012; Braun and Kaitna, 2016; Turkington et al., 2016; van den Heuvel et al., 2016). Either approach works under the implicit conjecture that rainfall is the only hydrological factor controlling debris flow initiation. While this is likely to hold in rainfall-dominated, warm, humid climates (e.g. Köppen–Geiger climate classes Af, Am, Cfa, and Csb), it may carry substantial uncertainty in cooler, snow or rain-on-snow-dominated climates, often characterized by lower precipitation intensities (e.g. Dfa, Dfb, Dsa, Dsb), as both, relatively high-intensity snowmelt in spring to mid-summer and gradual soil moisture build-up through the warm season by persistent, lower-intensity rainfall and snowmelt, can add significant additional liquid water volumes to the subsurface of the system. This very likely leads to much less sharply defined rainfall intensity thresholds for debris flow initiation, as also to some degree reflected in the concept of variable hydrological disposition (Kienholz, 1995).
To circumvent the problem of data scarcity in mechanistic models to a certain
degree while at the same time bringing some more process knowledge into the
traditional intensity-duration thresholds and antecedent rainfall model
approaches, we here analyse the value of describing debris flow initiation as
a function of several contributing and potentially complementary hydrological
and meteorological variables. To do so, we here explore the potential of
zooming out to the macro-scale (cf. Savenije and Hrachowitz, 2017), using a
well-constrained, semi-distributed conceptual rainfall–runoff model to
analyse and quantify these individual variables and their potentially
temporally varying importance as additional contributions for the initiation
of debris flows. Briefly, such a model generates time series of different
system state and flux variables, such as soil moisture or snowmelt. As these
variables explicitly reflect the combined and temporally integrated
influences of different interacting individual processes, this approach
allows a more complete and detailed picture of the processes involved. For
example, as recently emphasized by Bogaard and Greco (2016), using the
modelled soil moisture to replace the general concept of antecedent wetness
has the advantage of both explicitly
In this exploratory, proof-of-concept paper we test for a catchment in the Austrian Alps (Köppen–Geiger class Dfb) the hypotheses that time series of system state and flux variables generated with a semi-distributed model, used together with observed meteorological variables, can contain enough information (1) to discriminate between distinct contributing factors to debris flow trigger mechanisms, and (2) to identify intra-annual shifts in the relative importance of these distinct mechanisms to understand at which time in the year traditional rainfall intensity-duration thresholds (e.g. Guzzetti et al., 2008) may exhibit reduced predictive power.
The Pitztal, situated in the south-western Austrian province of Tyrol, is a
side valley of the Inn River. The longitudinal inner Pitztal (Figs. 1 and 2; TIRIS, 2015)
features a narrow valley bottom with steep hillslopes. The study area
(approximately encompassing the inner Pitztal) is about 20 km long in its
north-eastern extension, with an average width of 6.5 km, covering an area
of 133 km
Study area with locations of observed debris flows (centre of deposition), location of stream gauges and weather stations (debris flows: BMLFUW; gauging stations: TIWAG; weather stations: HD Tirol, TIWAG, ZAMG; land cover data: CORINE Land cover; glacier data: Austrian Glacier Inventory; rivers and location of catchment: TIRIS).
Photograph of the inner Pitztal, located next to Plangeroß.
Mean annual precipitation in the inner Pitztal is about 1330 mm a
Available hydro-meteorological data included daily time series of
precipitation (
Data availability, modelled study period and number of days with known debris flow occurrence. Only those debris flow events are plotted of which the exact date of occurrence was known, i.e. which were used for this study.
The daily precipitation input was calculated as the weighted mean of the
stations
We restricted the hydrological modelling to the relevant study area,
specifically adapting the hydrological model to the geomorphologically
homogeneous inner Pitztal. We thereby avoided the need to model the
extensively glaciated valley head and the outer Pitztal, where no significant
debris flow activity was recorded. To do so, daily discharge data from the
stations
In addition, daily snow depth measurements for the whole study period
1953–2012 were available from stations
Within the study period, 1953–2012, 81 debris flow events in the inner Pitztal have been documented by the Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management (BMLFUW, 2015; cf. Hübl et al., 2008). For 43 debris flows (Fig. 1) occurring on 25 individual event days (hereafter referred to as “events”) the date of occurrence was known (Fig. 3) and could thus be used for the detailed analysis of the trigger conditions in this study. For the statistical assessment of debris flow occurrence, however, the full set of 81 debris flow events, i.e. also including those for which only the year or month of occurrence was known, was taken into account.
Structure of the semi-distributed (stratification into 100 m elevation zones) hydrological model. Black symbols indicate fluxes and states, black underlined symbols indicate model input, and grey symbols indicate model parameters (for abbreviations, see Table 1).
To estimate otherwise unavailable hydrological state and flux variables at the time of debris flow occurrences, we implemented a semi-distributed conceptual rainfall–runoff model on a daily basis.
Adopting a flexible modelling strategy (Clark et al., 2011; Fenicia et al., 2011), which has proven highly valuable for many studies worldwide in the past (e.g. Leavesley et al., 1996; Wagener et al., 2001; Clark et al., 2008; Fenicia et al., 2014, 2016; Gharari et al., 2014; Hrachowitz et al., 2014), we customized and extensively tested a range of functionally different model structures and parameterizations (not shown). The most suitable of these tested model structures, which was subsequently used for the study catchment (Fig. 4), has nine free calibration parameters (Table 1b) and resembles the wide-spread HBV type of models, which were previously successfully applied over a wide range of environmental conditions (e.g. Seibert, 1999; Seibert and Beven, 2009; Fenicia et al., 2014; Berghuijs et al., 2014; Birkel et al., 2015; Hrachowitz et al., 2015; Nijzink et al., 2016b). All model equations are provided in Table S1 in the Supplement.
Briefly, the model was implemented with a semi-distributed snow routine,
stratified into 100 m elevation zones. In the absence of more detailed data,
the volume of water falling as snow (i.e. solid precipitation
Rain (i.e. liquid precipitation
The model at hand thus consists of a semi-distributed, elevation-stratified snow routine and a lumped hillslope component. While we tested different levels of spatial distribution due to different hydrological response units, including for example a parallel wetland component, we decided to go for the most parsimonious feasible model architecture, since more complex models neither improved model performance nor notably influenced the runoff behaviour. As flow velocities are very high, due to the elevated elevation gradients, and flow distances are relatively short, channel routing was considered negligible on the timescale of the implementation. Similarly, interception was neglected due to the limited amount of forested areas.
Model calibration, based on Monte Carlo sampling with 10
In the absence of more detailed information, all three objective functions in
The best performing 0.1 % of parameter sets in terms of
The period 2007–2012 was thereafter used for post-calibration model testing
and evaluation (“validation”; Fig. 3), based on the set of retained
solutions and their performance metrics
To identify potentially different triggers for debris flow initiation, we
then explored a range of hydro-meteorological system variables at days
For the observed system variables
The analysis of the modelled system variables was based on the behavioural
parameter sets, which were used to generate distributions of values for each
variable at the days of debris flow events occurring. The material presented
hereafter is limited to
To be able to assess the variables' magnitude at debris flow initiation, we compared the magnitude of each system variable with the marginal distributions (i.e. distributions generated with the time series of all days, namely event days and non-event days; see also below) of the respective variables, allocating an “exceedance probability” to each value, rather than looking at the absolute numbers. Due to the generally very low occurrence probability of debris flow events and gaps in the data records (i.e. 25 well-documented events over 60 years), which potentially may in the following lead to instable and overly discontinuous statistical models, we limited the definition of exceedance probabilities (and all other probabilities estimated hereafter) to the period of the year in which all debris flow events occurred (“debris flow season”), i.e. from 15 May to 15 October 1953–2012. In other words, all probabilities reported hereafter are conditional on that period.
To facilitate a more objective and quantifiable comparison of the system
variables, classes of exceedance probabilities were defined for the
individual variables, with exceedance probabilities
1
Using the exceedance probabilities of the three system variables daily
precipitation
By comparing the values reached at debris flow initiation with the marginal distribution of the variables we applied a probabilistic concept (cf. Berti et al., 2012), which does not only consider the days where debris flows were reported, but also the non-event days. This, in turn, allowed an assessment of whether the respective variables were significantly increased, and thus likely to be (partially) responsible for the debris flow triggering. Please note that we on purpose do not provide any explicit posterior probabilities for debris flows in our main analysis, due to the limited sample size and the focus of the paper not being on providing probabilities o debris flow occurrence (and thus a blueprint for a prediction model), but to analyse the event's triggering conditions.
The retained behavioural parameter sets (see posterior parameter
distributions in Table 1) generated model outputs that reproduced the
features of the hydrological response in a generally plausible way, as can be
seen in Fig. 5 for some selected years and in Fig. S2 for the remaining years
of the study period. This is on the one hand reflected in the rather elevated
performance metrics for streamflow. The models' best fit overall objective
function reached
Observed daily streamflow
In the following the values of hydro-meteorological variables at the days of
debris flow occurrences were extracted from the observed and modelled time
series. On 3 out of the 25 days with debris flows (nos. 7, 11, 19), the
observed precipitation at all three rain gauges exceeded
High modelled snowmelt rates with
Plots of relevant system variables:
Similarly, the mean modelled antecedent soil moisture
The exceedance probabilities presented above of several system variables at
days of debris flow occurrence allowed us to estimate the changing relative
relevance of
On the 3 event days with precipitation totals of
For event nos. 21 and 24, heavy precipitation was likely to have a very high
relevance as a contributor to triggering debris flows, as well (Table 2).
This is in spite of the catchment average observed precipitation on these
days being less extreme, with 0.01 <
The 25 recorded debris flow events in the inner Pitztal that
occurred at known dates since 1953. For each event the exceedance
probabilities
Event nos. 8, 9 and 10 occurred on days when the modelled snowmelt reached
exceedance probabilities of
For no. 17, an extremely low snowmelt exceedance probability of
Mirroring the reasoning for event nos. 8, 9 and 10, the snowmelt exceedance
probabilities of 0.01 <
To sum up, event nos. 2, 8, 9, 10, 17, and 20 have been associated with snowmelt as the primary trigger, while the assumed additional influence of rainfall (i.e. “rain-on-snow”) and antecedent soil moisture varies between the events. Additional supporting evidence for the above reasoning is that the general timing of the above events coincides well with the snowmelt season. Snowmelt typically peaks during May and June in the study region (Figs. 5 and S2), while high-intensity, convective rainfall is mostly only observed later in the season (i.e. July and August).
For event no. 13, the gradual build-up of soil moisture
A similar pattern can be found for event no. 6, albeit with a lower relative
contribution from soil moisture, whose contribution to trigger the event was
moderately relevant (
Interestingly, both events, nos. 6 and 13, occurred in the lowest part of the study area, where relatively large parts are vegetated (Fig. 1), while most of the events associated with high-intensity precipitation (nos. 1, 3, 4, 5, 12, 14, 15, 16, 18, 21, 22, 23, 24, and 25) took place at higher elevations. For these events, the antecedent soil moisture estimates have been mostly below average, which not only backs the interpretation of high-intensity precipitation as dominant trigger (as discussed in Sect. 4.3.1), but may also indicate that the antecedent soil moisture is in general of minor significance at higher elevations, as in it headwaters the catchment is dominated by lower-permeability surfaces (bare rock, sparsely vegetated areas) and shallow soils that only provide limited storage capacities (cf. Berti and Simoni, 2005; Coe et al., 2008; Gregoretti and Fontana, 2008).
The above analysis illustrated quite clearly that water inputs originating
from different individual “sources” can significantly contribute to
generate trigger conditions in the study area. The data further suggest that
the relative relevance of each these variables contributing to the actual
trigger conditions does vary over time. Even more, there is some evidence
that among the three tested variables, high-intensity and potentially
short-duration precipitation
Individual exceedance probabilities
Debris flow events by month of occurrence and likely dominant trigger; shades indicate the relative strength (the darker the stronger) of the dominant trigger in terms of (1) its relative relevance compared to the other contributing variables and (2) the extent to which it is directly supported by data (see also Table 2).
A somewhat different, more quantitative perspective is given by Fig. 7,
showing the joint conditional posterior probabilities of a debris flow event
Most debris flow events in the study area occur between mid- and late summer
(Fig. 8), when spring precipitation and persistent snowmelt have developed
above-average soil moisture levels and when the frequency of high-intensity,
convective rain storms increases (Figs. 5 and S2). Further analysis also
revealed a relatively clear pattern in the seasonally changing relative
relevance of the three considered variables as contributors to debris flow
trigger conditions. In general, three distinct seasonal debris flow trigger
regimes emerge from the analysis, which to a high degree reflect both the
seasonal cycle in the hydro-meteorological conditions and in debris flow
occurrence, from snowmelt- to convective-rainfall-dominated debris flow
triggers. While late spring and early summer events are mostly associated
with snowmelt in combination with elevated soil moisture and only very minor
contributions of high-intensity precipitation, the latter is, for the above
reasons, the dominant trigger in summer and early autumn. While the former
may be trivial given that significant snowmelt is less common from July
onwards, it is interesting to observe that high-intensity precipitation may
be, though also sometimes occurring in spring and early summer, less relevant
for triggering debris flows at that time of the year. In our dataset, event
no. 7 occurring in early June 1965 was attributed to high-intensity rainfall,
while event nos. 8–10, occurring in the same month, were predominantly
triggered by snowmelt, forming a clear exception to this general rule. Also,
in the same month, triggering by elevated soil moisture conditions due to the
combined effect of long-lasting rainfall and snowmelt has been observed
(event no. 6). This shows how debris flow triggers can change very rapidly
following weather changes. The general pattern (high-intensity precipitation
in summer vs. snowmelt in spring as the dominant debris flow triggers) mostly
arises from a combination of two factors, namely that in spring considerable
proportions of precipitation observed at lower elevations (1) still fall as
snow, in particular at higher elevations, and (2) are, if falling as rain,
intercepted by, transiently stored in and/or potentially refrozen in the
snowpack, in particular if the snowpack has not yet reached isothermal
conditions at 0
We would like to reiterate here that, as in any hydrological study at scales
larger than the hillslope scale, the issue of epistemic errors in data
(Beven, 2012; Beven et al., 2017a, b), arising from the typically
insufficient spatial but also temporal resolutions of the available
observations (mostly precipitation) can introduce considerable uncertainty in
the interpretation of a specific hydrological system (e.g. Valéry et al.,
2010; Nikolopoulos et al., 2014; Marra et al., 2017) which is further
exacerbated by complex, mountainous terrain (e.g. Hrachowitz and Weiler,
2011). This is in particular relevant for debris flows as they depend on the
hydrological conditions at the specific location of their initiation, which
is frequently of very limited spatial extent. Borga et al. (2014), for
instance, reported the occurrence of several debris flows that were triggered
by highly localized, high-intensity rainfall > 100 mm h
We also explicitly acknowledge additional uncertainties arising from the use of a simple, semi-distributed model to represent the hydrological system of the study area. Such models are clearly oversimplifications of the detailed processes controlling the storage and release of water. Together with the effect of the above discussed data errors, this explains, why the model cannot fully reproduce some of the features in the observed hydrograph (e.g. Figs. 5b, c and 6f), in spite of its adequate overall performance. Indeed, out of the 6 debris flow days, where both modelled and measured runoff values were available, the modelled runoff in three cases did not correspond particularly well to the measured runoff (Fig. 6f), although in those cases where the runoff was underestimated by the model (no. 20), this was most likely due to unrecorded or underrecorded precipitation. This ambiguity equally affects the estimates for total soil moisture, while the modelled snowmelt and the antecedent soil moisture, in contrast, can be assumed to be more correct, as these variables are integrations over time, in which case erroneous precipitation measurements are likely to be compensated and thus of less consequence (e.g. Hrachowitz and Weiler, 2011). In addition, the spatial integration of local processes is likely to result in a misrepresentation of hydrological conditions for the locations of debris flow initiation.
However, even though the model is rather simple with limited spatial differentiation, we would like to point out that our approach is not due to an ill-advised oversimplification. Rather, it is the (un-)available data that limit a meaningful spatial differentiation. The most crucial meteorological input, namely precipitation, is very often (and also here) not available on a spatially sufficiently distributed basis (see above), let alone for the actual source area of a specific debris flow. Furthermore, the calibration of a more distributed model would be more problematic and – in the case of fully distributed physically based models – would encounter many other sources of uncertainties (e.g. model/parameter equifinality, scale of available field observations of physical parameters vs. scale of the modelling application/grid size, the suitability of the model equations for the scale of the applications). These issues have been acknowledged for quite some time, but no real progress to close the gap between simplicity and complexity has yet been made (e.g. Dooge, 1986; Beven, 1989, 2006b; Jakeman and Hornberger, 1993; Sivapalan, 2005; McDonnel et al., 2007; Zehe et al., 2007, 2014; Clark et al., 2011, 2017; Hrachowitz and Clark, 2017).
More specifically and notwithstanding these limitations, the catchment-wide
considerable melt rates M, together with the generally elevated soil moisture
The results of this study suggest that the available, relatively scarce data and the semi-distributed model together contained sufficient information to facilitate an analysis that allowed the identification of general, large-scale patterns and thus the distinction of three different relevant “sources” of water, i.e. high-intensity precipitation, snowmelt and antecedent soil moisture, that contribute with varying relative importance to the initiation of debris flows. In the study region, high-intensity rainfall as a trigger was mostly limited to mid- and late summer, while snowmelt could be identified as the dominant trigger in late spring/early summer. This highlights the value of a more holistic perspective for developing a better understanding of debris flow formation and may provide a first step towards more reliable debris flow predictions, in particular for snow-dominated regions.
The model code used can be made available by the first author upon request.
Hydrological data may be requested from HD
Tirol (
The supplement related to this article is available online at:
KM, RK and MH designed the study, KM and DP carried out the analysis, and MH, KM and RK wrote the paper.
The authors declare that they have no conflict of interest.
We thank HD Tirol, TIWAG and ZAMG for supplying the climate datasets and Martin Mergili for readily sharing his rainfall lapse rate data. We would also like to thank the reviewers for their thoughtful and interesting comments and suggestions, which substantially improved this paper. This project receives financial support from the Austrian Climate and Energy Fund and is carried out within the framework of the ACRP programme. Edited by: Matjaz Mikos Reviewed by: three anonymous referees