A Remaître

C Van Westen

J Ettema

J P Malet

T Turkington

Thea Turkington

CiteSeerX

International Conference  
Analysis and Management of Changing Risks for Natural Hazards  
18‐19 November 2014  l Padua, Italy 
 
AO2 ‐ 1 
 
Abstract code: A02 
 
Impacts of climate change on debris flows – what are some of the challenges? 
 
T. Turkington1, J. Ettema1, J.P. Malet2, A. Remaître2, C. van Westen1 
 
1ITC, University of Twente, Enschede, Netherlands 
2 Institut de Physique du Globe de Strasbourg, University of Strasbourg, Strasbourg, 
France 
 
Corresponding author details: 
Thea Turkington, University of Twente email: t.a.r.turkington@utwente.nl 
 
Keywords: 
Climate change, downscaling, debris flows, uncertainty 
 
Extended Abstract (10 pages max):  
 
INTRODUCTION 
 
While changes in temperature and precipitation will likely affect debris flows, developing 
relevant future climate projections remains challenging. Three of these challenges are 
considered in this work: linking past debris flows with the meteorological trigger, how to 
approach the necessary downscaling of global climate projections, and finally, dealing with 
uncertainty. To illustrate each of the three challenges, examples are taken from two 
European alpine catchments: Barcelonnette in the French Alps, and Fella River in NE Italy. 
 
 
CHALLENGES LINKING CLIMATE CHANGE AND DEBRIS FLOWS 
 
Linking past debris flows and meteorological triggers 
To develop climate projection, the hazards should be linked with meteorological properties or 
conditions that climate models are able to reproduce. Rainfall intensity-duration thresholds 
are often applied to determine minimum rainfall conditions under which debris flows may 
occur (see Guzzetti et al. (2008) for a review of different methods). However, while debris 
flows are often preceded by intense rainfall, capturing the rainfall in mountains catchments 
can be difficult due to the lack of rain gauges or poor coverage by weather radar in 
mountainous regions.  
 
The relationship between measured precipitation and debris flow (DF) events is different for 
Fella River and Barcelonnette. One similarity though, is that for both areas, the rain gauges 
are all in the bottom of the valleys, rather than on the slopes where the debris flows originate 
and where potentially the rainfall is more intense. In Barcelonnette, this results in few debris 
flow events being associated with extreme 1-day rainfall, although some rain was recorded 
before most events (Figure 1 – Barcelonnette (a)). Furthermore, some of the events occur 
during the spring, where snowmelt is also a contributing factor, and is often not captured in 
rainfall thresholds. In the case of Fella River, most debris flows and shallow landslides occur 
in the late summer and autumn, as well as daily precipitation totals are higher than in 
Barcelonnette. This results in all DF events being associated with heavy or extreme 1-day 
rainfall, and few of the most extreme rainfall events with no debris flows or shallow landslides 
reported (Figure 1 Fella (a)).  
 
International Conference  
Analysis and Management of Changing Risks for Natural Hazards  
18‐19 November 2014  l Padua, Italy 
 
AO2 ‐ 2 
 
When measure precipitation and DF cannot to be linked, there are other possibilities. 
Previous work demonstrated how atmospheric variables, such as specific humidity (Q) and 
estimates of atmospheric instability (CAPE) can be used as proxies for rainfall (Turkington et 
al., 2014). Figure 1 Barcelonnette (b) shows how by using Q and CAPE instead of daily 
rainfall, more debris flow events are associated with extreme values. For Fella, using CAPE 
and Q is also possible (Figure 1 Fella (b)); however, the most accurate threshold uses CAPE 
and 1-day rainfall (not shown).  
 
 
 
Figure 1 Rainfall events with dates of debris flows circled. For Fella (left), the inventory also includes shallow landslides.  The 
blue and green lines give the moderate and high thresholds for debris flows respectively. The high threshold represents the 
threshold where the highest percentage of days above the threshold are associated with DF events, while the moderate 
threshold is similar with the added condition that at least 50% (Fella) or 30% (Barcelonnette) DF events are also above the 
threshold. 
While rainfall or atmospheric variables can be used to link debris flows and climate, it is 
important to recall that there are other influencing factors. Other influencing factors, such as 
land cover, make the precise meteorological trigger dependant on the catchment. Using 
hourly precipitation data can improve the results, however, changes in sub-daily precipitation 
depends on the length of the observed record, and the ability to obtain climate projections at 
this resolution. In the end, once a link between the local climate and debris flows is achieved, 
it can be used as a basis for the climate projections. 
 
 
International Conference  
Analysis and Management of Changing Risks for Natural Hazards  
18‐19 November 2014  l Padua, Italy 
 
AO2 ‐ 3 
 
Downscaling of global climate projections 
General Circulation Models (GCMs) are often at a much coarser scale than most debris flow 
source areas, with variables available at daily or coarser time-scales. By downscaling global 
projections, changes in the relevant meteorological variables can be examined at a finer 
scale. This is especially important for mountainous regions where orographic processes are 
not always adequately captured in the global models. However, there are a variety of 
downscaling techniques, each with different assumptions, and potentially producing different 
results. Three different methods are demonstrated here for the two case study areas based 
on the thresholds shown in Figure 1: dynamical downscaling, statistical downscaling and 
atmospheric variables. Further information on the first two downscaling methods for 
precipitation can be found in Maraun et al. (2010). 
 
Dynamical downscaling 
Dynamical downscaling physically models the climate at finer resolutions than the global 
scale GCMs. Recently, new data became available through the CORDEX project, with 
multiple regional climate models (RCMs) for Europe and other areas around the globe 
(Jacob et al., 2014). Figure 2 shows the change in number of days above the moderate and 
high threshold rainfall threshold from Figure 1 for each of the study areas based on different 
combinations of RCMs, GCMs, and emission scenarios. In Fella, for both the moderate and 
high thresholds, the number of events either remains constant, or increases. Under the high 
threshold, where every event between 1990 and 2011 was associated with one or more 
debris flows, two models show and increase in frequency by a factor of five. For 
Barcelonnette however, the RCM projections are less consistent. For the moderate 
threshold, some RCMs indicate and increase in frequency, while for others, there is possibly 
a decrease. The high threshold events are similar to Fella though, where the projections 
show a constant number of events or an increase.  
 
Figure 2 Future projections for different rainfall events for Fella (left) and Barcelonnette (right). The moderate and high 
thresholds are the same as those in Figure 1. The observed frequency of rainfall events is given in black. The equivalent 
number of DF events based on the empirical relationship is given on the right y‐axes.  
International Conference  
Analysis and Management of Changing Risks for Natural Hazards  
18‐19 November 2014  l Padua, Italy 
 
AO2 ‐ 4 
 
 
Statistical downscaling 
Statistical downscaling uses the relationship between large scale atmospheric conditions 
(predictor) and the local scale variable (predictand) to predict how the predictand, in this case 
precipitation, may change in the future.  The basic assumption being that the relationship 
between the predictand and the predictor holds true under future conditions as well as for the 
present. There are a variety of different statistical downscaling techniques. For this paper two 
different statistical downscaling techniques were used: analogues and generalised linear 
models (GLMs).  Weather typing splits the data into different groups or ‘weather types’ based 
on the synoptic conditions separated using k-means clustering. Different atmospheric 
clustering methods are reviewed in Huth et al. (2008).  For GLMs, the predictand is assumed 
to be a linear combination of the predictors, where the predictand does not necessarily have 
to be normally distributed, such as is the case for precipitation (Fealy and Sweeney, 2007).  
 
 
Figure 3 Future projections for different rainfall events for Fella (left) and Barcelonnette (right) using statistical downscaling. 
The moderate and high thresholds are the same as those in Figure 1 and Figure 2. The observed frequency of rainfall events 
is given in black. The equivalent number of DF events based on the empirical relationship is given on the right y‐axes. 
For the two study areas, statistical downscaling also produces a range in change in 
frequency for the rainfall events (Figure 3). The spread of results using statistical 
downscaling is similar to those in Figure 2 for Fella River (Figure 3 left), with either no 
change, or an increase in frequency. For Barcelonnette, most of the projections show little 
change in the frequency of events (Figure 3 right). This is different for the Barcelonnette- 
High threshold events in Figure 2, where based on dynamical downscaling the projections 
have a tendency towards an increase in rainfall events, while based on SD it is generally 
constant, or in one case, a strong decrease.  
 
Atmospheric variables 
The two previous methods are based on changes in precipitation; however, the relationship 
between daily measured precipitation and debris flows for Barcelonnette was not as strong 
International Conference  
Analysis and Management of Changing Risks for Natural Hazards  
18‐19 November 2014  l Padua, Italy 
 
AO2 ‐ 5 
 
as when using atmospheric variables (Figure 1). Therefore, it is possible that future 
projections of debris flows may differ from those obtained using precipitation. 
 
Starting with only two GCMs and one future scenario (RCP85) from the CMIP5 project 
(Taylor et al., 2011), Figure 4 shows the change in Q700 and CAPE for Barcelonnette, based 
on the thresholds found in Figure 1 Barcelonnette (b). The increase in number of events is 
much great than any of the projections in Section 3.1 and 3.2, which focused on changes in 
precipitation. However, this is only a preliminary result. It is possible that these projections 
are outliers, and that by including more GCMs and scenarios, the increase in number of 
events on average is much lower. Furthermore, using a finer scale RCM data may show that 
CAPE on a finer spatial scale does not change as dramatically as averaged over a larger 
area. Further work using more projections and finer scale models is needed before 
substantial conclusions can be made. Even so, Figure 4 demonstrates the potential for 
intense local convection to increase, while overall precipitation decreases.  
 
 
Figure 4 Number of events per year based on the approximate CAPE value and Q700 for two projections. The observed 
number of events 1979‐2010 is shown in black. The top figure is for the moderate threshold in Figure 1 Barcelonnette (b), 
and the lower for the higher threshold in Figure 1 Barcelonnette (b). 
 
DISCUSSION 
 
The results presented here show a wide range of future projections. The spread comes from 
the different sources of uncertainty in climate modelling, including from the models 
themselves, emission scenarios, as well as the observations. By also investigating changes 
at finer scales, there is also often a large spread in results from more variability in the 
observations. Furthermore, for debris flows, there is also the uncertainty in the link between 
the meteorological trigger and the debris flow events. Not considered in this work were other 
changes, such as land cover, to debris flows in the area.    
 
Due to the spread of future projections, it is important to use multiple GCMs, downscaling 
techniques, and even meteorological triggers, to capture the spread. Other studies have 
International Conference  
Analysis and Management of Changing Risks for Natural Hazards  
18‐19 November 2014  l Padua, Italy 
 
AO2 ‐ 6 
 
found similar results. Looking at groundwater recharge, Crosbie et al. (2011) found that 
downscaling technique, GCM, and to a lesser extent the hydrological model, all impacted the 
results. Schmidli et al. (2007) also found differences in future precipitation for the European 
Alps using RCMs and statistical downscaling. Therefore, when considering climate change 
impacts for debris flows, multiple GCMs, as well as downscaling techniques should be taken 
into account. 
 
A challenge then becomes how to incorporated climate projections into risk assessments. 
While it may be unrealistic to include all the different projections when considering climate 
impact assessments, the results can be used as markers for sensitivity studies. Furthermore, 
a sub-set of the projections could be used, including some of the projections that show the 
most dramatic shifts, as these may be more relevant in risk assessment (Raff et al., 2009). 
 
CONCLUSION 
 
This work introduced different challenges in developing projections of debris flows for two 
study areas: Fella River, NE Italy, and Barcelonnette, France. These challenges included 
linking debris flows and the triggering conditions, as well as downscaling large scale climate 
projections. The work considered only rainfall-triggered debris flows, while snowmelt also 
plays a role in triggering events in the two areas, an area for future research. Future 
projections for Fella showed that debris flows triggers may remain constant or increase, while 
for Barcelonnette, it depended on the method used. In the end, the results here showed that 
the projections differ not only from different downscaling techniques and driving scenarios, 
but also the meteorological trigger chosen. Therefore, care is needed for both assessing how 
the climate may change, as well as how the climate interlinks with the debris flow or other 
natural hazard. 
 
 
ACKNOWLEDGEMENTS 
  
This work was undertaken as part of the EU FP7 Marie Curie ITN project CHANGES. Data 
used in this study were provided by Météo France, and the Hydraulic Service, FVG, Italy. 
And we also acknowledge the World Climate Research Programme's Working Group on 
Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of 
CORDEX and responsible panel for CMIP5. We also thank the climate modelling groups for 
producing and making available their model output. 
 
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Analysis and Management of Changing Risks for Natural Hazards

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