Adel Noureddine

Aurelien Bourdon

Romain Rouvoy

Lionel Seinturier

A Preliminary Study of the Impact of Software Engineering on GreenIT

Keywords: Performance, Measurement, Experimentation, Energy, Power Model, Monitoring

GreenIT has emerged as a discipline concerned with the optimization of software solutions with regards to their energy consumption. In this domain, most of state-of-the-art solutions offer limited or constraining approaches to monitor the energy consumption of a device or a process. In this paper, we therefore report on a runtime energy monitoring framework we developed to easily report on the energy consumption of system processes. Concretely, our approach adopts an OSlevel library, called POWERAPI, which estimates the power consumption of processes according to different dimensions (CPU, network, etc.). In order to better understand potential energy leaks of legacy software, we use this library to study the impact of programming languages and algorithmic choices on the energy consumption. This preliminary study is based on an empirical evaluation of a eight implementations of the Towers of Hanoi problem.

I. INTRODUCTION

Energy-aware software solutions and approaches are becoming broadly available, as energy concerns are becoming mainstream. The increasing usage of computers and other electronic devices (e.g., smartphones, sensors) is continuously impacting our overall energy consumption. Information and Communications Technology (ICT) accounted for 2% of global carbon emissions in 2007 [START_REF] Gartner | Green IT: The New Industry Shockwave[END_REF] or 830 M tCO 2 e [START_REF] Webb | SMART 2020: enabling the low carbon economy in the information age[END_REF], and is expected to grow to 1,430 M tCO 2 e in 2020 [START_REF] Webb | SMART 2020: enabling the low carbon economy in the information age[END_REF]. These values illustrate the opportunities for efficient ICT solutions to reduce carbon emissions and energy consumption.

Rising energy costs in computers and mobile devices implies the optimization and the adaptation of computer systems. In this domain, research in GreenIT already proposes various approaches aiming at achieving energy savings in computers and software. In particular, monitoring the energy consumption of the system is a requirement to achieve such savings. However, most of the state-of-the-art approaches either focus on the hardware [START_REF] Mcintire | ETOP: sensor network application energy profiling on the LEAP2 platform[END_REF], or offer coarse-grained energy feedbacks of software [START_REF] Flinn | PowerScope: A Tool for Profiling the Energy Usage of Mobile Applications[END_REF], [START_REF] Do | pTop: A Process-level Power Profiling Tool[END_REF].

In this paper, we therefore propose to gather applications energy feedback information at runtime and with similar accuracy as hardware equipments while using only a software approach. Our approach consists of a system monitoring library (at the operating system level), called POWERAPI. POWERAPI estimates the energy consumption of running processes, in real-time, based on raw information collected from hardware devices (e.g., CPU, network card) through the operating system. We use both state-of-the-art energy models and propose new models to compute the energy consumption of software.

Using this monitoring framework, we compare the energy footprint of eight implementations of the Towers of Hanoi program. Our preliminary results demonstrate that we can clearly observe the impact of different implementations of algorithms on the energy consumption, thus providing the opportunity at a later stage to reduce their energy footprint.

The remainder of this paper is organized as follows. In Section II, we describe our motivations and the main challenges we tackle. Section III describes our approach, the design of our proposed architecture and our energy models. Section IV details the implementation and validation of our prototype. In Section V, we report and discuss the preliminary results we obtained from comparing the Towers of Hanoi implementations. Related work is discussed in Section VI, while we conclude in Section VII.

II. MOTIVATION AND CHALLENGES

According to [START_REF] Vereecken | Overall ict footprint and green communication technologies[END_REF], ICT consumes up to 7% of global power consumption (or 168 GW) in 2008. This number is predicted to grow and double to 433 GW in 2020 or more than 14.5% of worldwide power consumption [START_REF] Vereecken | Overall ict footprint and green communication technologies[END_REF]. With the rise of ICT power consumption, power-aware optimizations and management cannot be done efficiently without accurate power measurements. In particular, we argue that monitoring the power consumption of applications (and not only hardware) has become a requirement for power-aware Software as a Service (SaaS) and power-aware software adaptation.

Hardware monitoring is usually achieved through additional hardware measurement equipments, such as multimeters or specialized integrated circuits (cf. Section VI). This approach offers a precise and accurate measurement of the energy consumption of hardware components, but at the cost of an additional investment. Furthermore, it cannot monitor the energy consumption of software components and running applications.

We rather believe that a scalable approach can be achieved by a software-centric approach. Monitoring the energy con-sumption of software has to yield many challenges in order to build an accurate software-centric approach. In particular, the biggest problem that software monitoring tools face is providing accurate estimations of energy consumption based on collected raw information. Unlike hardware measurements, software approaches use energy models in order to provide an estimation of the energy consumption of software components. However, these estimations tend to have different degrees of accuracy and overhead.

In addition to accuracy, the overhead of the monitoring platform is to be optimized in order to limit its impact. The overhead depends both on the degree of accuracy needed and on the cost of the monitoring tool and the monitored applications. This leads to a difficult tradeoff between the accuracy requirements and the cost of the software monitoring tool.

Laying these challenges, we propose in the next section an approach named POWERAPI for monitoring applications and processes at runtime.

III. POWERAPI DESIGN AND APPROACH

In this section, we present the POWERAPI general architecture and we describe the approach we use for defining our energy models. The architecture is based on a modular approach, mixing power monitoring tools with energy models in order to provide energy information of software.

A. Architecture

POWERAPI architecture is a modular architecture built for agile software programming. The core of the architecture are power modules. POWERAPI is constructed as separate modules that can be started or stopped at runtime upon needs. A set of OS-dependent sensor modules (e.g., CPU, network) collect raw information about hardware resource utilization, either directly from the devices or through the operating system. This information is then exposed to another set of OS-independent formula modules that use our power models (cf. Section III-B) to compute the power consumption of each hardware component. These modules also compute the power consumption of running processes and applications per hardware resource. A local database is also used to store configurations about the hardware resources and used to auto-calibrate POWERAPI depending on the environment. Finally, all these modules are managed by a life cycle module. The latter allows to start, stop, add, remove or modify modules depending on monitoring needs and commands sent by applications. Figure 1 depicts the overall architecture of POWERAPI.

B. Power Models

We propose a comprehensive energy model using our own proposed formulae and formulae taken from the state-of-the- art. In [START_REF] Seo | An energy consumption framework for distributed java-based systems[END_REF], the energy cost of software is computed based on the following formula:

E sof tware = E comp + E com + E inf ra
where E comp is the computational cost (i.e., CPU processing, memory access, I/O operations), E com is the cost of exchanging data over the network, and E inf ra is the additional cost incurred by the OS and runtime platform (e.g., Java VM).

We base our model on a similar principle, taking into account the modular aspect of the power calculation (e.g., the sum of the power consumption of different hardware components). Infrastructure power is included in the computational cost of our power models and in our prototype. From this, we can abstract our global power formula as follows:

P sof tware = P comp + P com (1)
At this stage, we developed two models, one for CPU computational costs and one for network communication costs. P comp is therefore equal to the CPU power consumed by software, and P com is equal to the power consumed by the network card for transmitting software's data. Next, we will detail the CPU and network energy models we use in POWERAPI.

1) CPU Model: The CPU power consumed by a specific process (in our case we use process P IDs) can be represented as:

P P ID CP U (d) = P CP U (d) × U P ID CP U (d)
Where P P ID CP U (d) is the CPU power consumed by the specific PID during a given duration d, P CP U (d) is the global CPU power during d and U P ID CP U (d) represents the process CPU usage during d. Thus, our approach is to estimate the power required by the CPU to execute the process PID. This is achieved by computing the CPU percentage usage of the PID by the overall CPU power during a given duration d. Next, we detail our model in order to compute P CP U (d), the global CPU power, and U P ID CP U (d), the process CPU usage.

Global CPU power: The overall power consumption for the majority of modern processors (CMOS 1 ) follows the standard equation:

P f,v CP U = c × f × V 2 (2)
where f is the frequency, V the voltage and c a constant value depending on the hardware materials (such as the capacitance). Power consumption is not always linearly dependent to the percentage of CPU utilization. This is due to DVFS (Dynamic Voltage and Frequency Scaling) and also to the fact that power depends on the voltage (and subsequently the frequency) of the processor. For example, A process at 100% CPU utilization will not necessarily consume more power than a process running at 50% CPU utilization but with a higher voltage. Therefore, a simple CPU utilization profiler is not enough in order to monitor power consumption. Our power model takes into consideration these characteristics of the CPU and allows accurate power consumption monitoring. According to formula 2, calculating the overall CPU power for a given time is equal to calculating a static part (the constant c) and a dynamic part (the frequency f and its associated voltage V). For the static part, the c constant is a set of data describing the physical CPU (e.g., capacitance, activity factor). Manufacturers may provide this constant, but in most of the cases this value is not available. To alleviate this problem, we use the existing relation between the overall power of a processor and its Thermal Design Power (TDP) value. TDP represents the power the cooling system of a computer is required to dissipate the heat produced by the processor. Therefore, the overall CPU power can be associated with the TDP as described by [START_REF] Rivoire | JouleSort: a balanced energy-efficiency benchmark[END_REF]:

P f T DP ,V T DP CP U 0.7 × T DP
where f T DP and V T DP represent respectively the frequency and the voltage of the processor within the TDP state. The benefit of using this formula is that TDP is a value provided by most manufacturers.

For the dynamic part, the frequency f is associated to a specific voltage V. One or more frequencies can be associated to a specific voltage. Lowering the voltage results in changing frequency. The other way around is also valid. Frequencies used by a processor are provided by the operating system APIs, while voltages are given by manufacturers.

Process CPU usage: In order to calculate the CPU usage for a given process (identified by its PID), we propose to calculate the ratio between the CPU time for this PID and the global CPU time (the time the processor is active for all processes) during a duration d:

U P ID CP U (d) = t P ID CP U t CP U (d) 1 Complementary Metal Oxide Semiconductor
Our approach is inspired by well-known tools, such as the top linux program2 . Thus, P comp of formula 1 and for the CPU power consumption in a duration d is equal to :

P comp = 0.7 × T DP f T DP × V 2 T DP × f × V 2 × t P ID CP U t CP U (d)
2) Network Model: The network power of a process is calculated using a formula similar to the CPU power formula. We base our model on available information whether they are collected at runtime or provided by manufacturers' documentations. As a first step, we focus on Ethernet network cards. A similar model using a linear equation can be applied for wireless network cards [START_REF] Feeney | Investigating the energy consumption of a wireless network interface in an ad hoc networking environment[END_REF], but we did not investigate wireless cards yet.

We obtain, from manufacturers' documentations the power consumed (in watt) for transmitting bytes for a certain duration (typically one second) according to a given throughput mode of the network card (e.g., 1 MB, 10 MB, etc.). Our network power model is therefore defined as:

P P ID network = i∈states t i × P i × d t total
where P i is the power consumed by the network card in the state i (provided by manufacturers), d is the duration of the monitoring cycle, and t total is the total time spent in transmitting data with the network card.

Based on these models, our formula 1 is now equal to:

P sof tware = 0.7 × T DP f T DP × V 2 T DP × f × V 2 × t P ID CP U t CP U (d) + i∈states t i × P i × d t total
IV. IMPLEMENTATION POWERAPI is implemented as a system level modular library. We implemented so far the CPU and network modules and associated power models.

Our system-level library aims to provide power information per PID for each system component (CPU, NIC 3 , etc.).

The library is therefore based on a modular approach where each system component is represented as a power module. Power modules operate independently of each other and are composed by two sub-modules: formula and sensor. These sub-modules communicate using the Service-Oriented Architecture (SOA) paradigm, and are deployed in an OSGi4 gateway. In particular, we use Service-Oriented Framework (SOF 5 ) to implement the various modules of POWERAPI in C++.

The sensor sub-module is responsible for gathering hardware and operating system related information for the module. For example, it gathers the number of bytes transmitted by the network card, and the time spent by the CPU for each of the processor frequencies (when DVFS is supported). This sub-module is OS-dependent. We implemented sensor submodules for the CPU and NIC on a GNU/Linux operating system. In particular, our implementation exploits system information available in the procfs [START_REF] Mouw | Linux Kernel Procfs Guide[END_REF] and sysfs [START_REF] Mochel | The sysfs Filesystem[END_REF] file systems.

The formula sub-module, on the other hand, is platform independent. The sub-module is responsible for computing the power consumed for each process by using information gathered by the sensor sub-module.

Additionally, our library supports the lifecycle management of its power modules. The latter can be started, stopped and their parameters changed at runtime, using a modules manager. The benefit of this modular approach is to offer flexibility while monitoring the system.

Validation

We validate the accuracy and precision of POWERAPI prototype on a Dell Precision T3400 workstation with an Intel Core 2 Quad processor (Q6600), running Ubuntu Linux 11.04. Our goal is to evaluate implementations of a CPU intensive algorithm, Towers of Hanoi. Therefore, we will only outline the results of the CPU module.

We compared the power values provided by POWERAPI with the actual power consumption of the computer using a bluetooth powermeter, PowerSpy 6 .

First, we stress the processor using the Linux stress command 7 . Figure 2 depicts the results as an evolution of the CPU power consumption along time (normalized values). The peaks correspond to stressing 1, 2, 3 and 4 cores, respectively. Figure 3 shows the accuracy of our library where we outline the measured values using the powermeter and the values provided by POWERAPI, excluding the preliminary synchronization values. We also compare the values of our library and the powermeter when viewing a video using MPlayer8 (results in Figure 4). Note that due to synchronization time lag between the Bluetooth powermeter and our library, values are shifted for a few seconds in the beginning of the monitoring. The time lag disappears after a couple of minutes.

The results show minor variations between the estimations computed by our library, and the real power consumption values. The margin of error is small in the core stressing and MPlayer scenarios at around 0.5% of the normalized and averaged values. Therefore, we can reasonably argue that using software-only approach, we provide values that are accurate enough to be used by energy management software.

V. IMPACT OF ALGORITHMS AND PROGRAMMING LANGUAGES

We run our POWERAPI library on a number of implementations of the Towers of Hanoi mathematical program, all taken from the hanoimania page 9 . We measured the power consumption and execution time of eight implementations: three benchmarks of the iterative implementation in C++ including one using the O2 and O3 10 GNU Compiler Collection (GCC) compiler options, three benchmarks of the recursive implementation in C (including O2 and O3), and recursive versions of Java, Python, OCaml, Pascal, Prolog and Perl programming languages.

POWERAPI provides power values (in watt), and we configured our library to take measurements at a 200 ms interval or 5 Hz, and we run the Towers of Hanoi program with 30 disks. Figures 5 and6 reports on the energy consumption (calculated in joule using the execution time) of the different implementations. Overall, we found that all implementations use up to 100% of one of the processor cores. In average, this equals to around 18 watts in our host configuration (ranging from 17.5 to 18.2 watts). However, the execution time varies greatly between the implementations and the algorithms.

Recursive and iterative versions: We first compare the difference between the recursive and iterative versions of the Towers of Hanoi program. The recursive algorithm implemented in C++ consumed in average 322.23 joules while its iterative version consumed 1656.26 joules, more than 400% increase. When using the O2 optimization option during compilation, both versions exhibit a similar energy consumption (on average 7% difference), however with the O3 option, the iterative version does not save any energy. The recursive version shows a 78% decrease in energy 9 http://www.kernelthread.com/projects/hanoi/ 10 http://gcc.gnu.org/onlinedocs/gcc/Optimize-Options.html consumption using the O3 option in comparison with the O2 option. O2 turn on more than 50 optimization flags in GCC and G++ compilers, while O3 adds 6 more flags including Predictive Commoning optimization (to eliminate redundancies across the iterations of a loop 11 ) that recursive algorithms benefit from in comparison to iterative ones. Our results show that the recursive version of the Towers of Hanoi program is more energy efficient than its iterative version.

Implementations in programming languages: In addition to energy efficiency differences in algorithm implementations, we observe that the energy consumed by the same algorithm varies from language to language. Both C and C++ recursive versions show similar results (55.45 joules for C and 53.20 joules for C++ with O3). The C recursive version consumed nearly 268% more energy than its Java counterpart. Compiling the program with the O2 optimization option allowed a gain of around 20%, but still 188% more energy consuming than Java. However, when compiling the program with all optimizations (option O3), energy consumption was greatly reduced to become even better than Java (37% decrease of C compared to Java).

Contrary to some beliefs, our results show that Java, using the default options, is quite energy efficient in comparison to other programming languages. Using only default values, the Java recursive version is the more energy efficient in our tests. Only the C recursive version with the O3 option performs better.

We also observed the energy efficiency of the Pascal language, being on par with the default C or C++. However, other results are more interesting: Perl is the most consuming (25 516 joules, executing for 23 minutes), Python being second (9 450 joules and less than 9 minutes), then Prolog (3 673 joules with 3.5 minutes), and finally Ocaml (with 1 375 joules and 1.2 minutes).

The difference between the most energy consuming (Perl) and the most energy efficient (C++ with O3) is quite high: 25 463 joules (or 47 863% increase). It is important to note here that our comparison tests are for one CPUintensive application (Towers of Hanoi). Other tests are needed in order to make a proper conclusion about the energy efficiency of a programming language compared to another. However, our results offers a preliminary indication of energy-efficiency trends in programming languages for CPU intensive applications. Nevertheless, implementing additional tests is one of our future work directions.

It is also important to note that although a CPU time profiler can offer an idea about the energy efficiency of a Tower of Hanoi program (due to the fact that the algorithm uses 100% of the processor nearly all the time), it is not always the case. Our test on a video playback on MPlayer (cf. Figure 4) shows a variation of power consumption of more than 130% increase. In this situation, a CPU time profiler is rendered less useful for energy monitoring. When adding other hardware resources (such as the video card or the memory), a comprehensive power model therefore becomes crucial for energy monitoring.

VI. RELATED WORKS

Monitoring energy consumption of hardware components usually requires an hardware investment, like a multimeter or a specialized integrated circuit. For example in [START_REF] Mcintire | ETOP: sensor network application energy profiling on the LEAP2 platform[END_REF], the energy management and preprocessing capabilities is integrated in a dedicated ASIC (Application Specific Integrated Circuit). It continuously monitors the energy levels and performs power scheduling for the platform. However, this method has the main drawback of being difficult to upgrade to newer and more precise monitoring and it requires that the hardware component be built with the dedicated ASIC, thus making any evolution impossible without replacing the whole hardware.

On the other hand, an external monitoring device provides the same accuracy as ASIC circuits and does not prohibit energy monitoring evolutions. Devices, such as ALERTME SMART ENERGY 12 , monitor home devices and allow users to visualize their energy consumption history through application services, such as the now defunct GOOGLE POW-ERMETER 13 . However, these approaches do not adapt the system autonomously: the user takes the decision, while our approach opens up solutions for adapting applications with limited user intervention.

The previous monitoring approaches allow getting energy measures about hardware components only. However, knowing the energy consumption of software services and components requires an estimation of that consumption. This estimation is based on calculation formulae as in [START_REF] Seo | An energy consumption framework for distributed java-based systems[END_REF] and [START_REF] Kansal | Fine-grained energy profiling for power-aware application design[END_REF]. In [START_REF] Seo | An energy consumption framework for distributed java-based systems[END_REF], the authors propose formulae to compute the energy cost of a software component as the sum of its computational and communication energy costs. For a Java application running in a virtual machine, the authors take into account the cost of the virtual machine and eventually the cost of the called OS routines. In [START_REF] Kansal | Fine-grained energy profiling for power-aware application design[END_REF], the authors take into account the cost of the wait and idle states of the application (e.g., an application consumes energy when waiting for a message on the network). In [START_REF] Flinn | PowerScope: A Tool for Profiling the Energy Usage of Mobile Applications[END_REF], the authors propose a tool, POWERSCOPE, for profiling energy usages of applications. This tool uses a digital multimeter to sample the energy consumption and a separate computer to control the multimeter and to store the collected data. POWERSCOPE can sample the energy usage by process. This sampling is more precise than energy estimation, although it still needs a hardware investment.

PTOP [START_REF] Do | pTop: A Process-level Power Profiling Tool[END_REF] is a process-level power profiling tool. Similar to the Linux TOP program, the tool provides the power consumption (in Joules) of the running processes. For each process, it gives the power consumption of the CPU, the network interface, the computer memory and the hard disk. The tool consists in a daemon running in the kernel space and continuously profiling resource utilization of each process. It obtains these information by accessing the /proc directory. For the CPU, it also uses TDP provided by constructors in the energy consumption calculations. It then calculates the amount of energy consumed by each application in a t interval of time. It also consists of a display utility similar to the Linux TOP utility. A Windows version is also available, so called PTOPW, and offers similar functionalities, but using Windows APIs.

In addition to PTOP, several utilities exist on Linux for resource profiling. For example, CPUFREQUTILS 14 , in particular CPUFREQ-INFO to get kernel information about the CPU (i.e., frequency), and CPUFREQ-SET to modify CPU settings, such as the frequency. iostat 15 that is used to get devices' and partitions' input/output (I/O) performance information, as well as CPU statistics. Other utilities [START_REF] Gite | How do I Find Out Linux CPU Utilization?[END_REF] also exist with similar functionalities, such as SAR, MPSTAT, or the system monitoring applications available in Gnome, KDE or Windows. In [START_REF] Amsel | Green tracker: a tool for estimating the energy consumption of software[END_REF], the authors propose GREEN TRACKER, a tool that presents an insight of the energy consumption of different software groups by estimating their CPU utilization. However, all of these utilities only offer raw data (e.g., CPU frequency, utilized memory) and do not offer power information.

Our approach is more flexible and evolutive than PTOP. In addition to offering process-level energy information, POWERAPI offers better flexibility and on-demand scaling of the tool. Monitoring modules can be shutdown or started depending on the context: on limited resources devices, modules, such as the network or hard disk modules, can be shutdown in order to monitor only the CPU. When more resources become available, these modules will be re-started. Other situations are also possible, such as situations where the user is only interested in monitoring the CPU or the network energy consumption.

VII. CONCLUSION AND FUTURE WORKS

In this paper, we present the POWERAPI architecture. It allows gathering and calculating the power consumption of processes and applications. We also propose energy models to calculate the energy consumption. Our models use and extend the state-of-the-art models and formulae. Our initial results show the potential of our approach for comparing, at runtime, the power consumption of applications and algorithms. As for future work, we plan to: i) propose more energy models for other hardware resources (in particular, memory and disk); ii) propose power monitoring at a finer grain, such as Java threads and methods; iii) extend our experimentation to more applications and algorithms, such as JETTY application server or network intensive applications; and iv) use power-aware information to adapt applications at runtime based on energy concerns.

Figure 1 .

 1 Figure 1. POWERAPI Reference Architecture.

Figure 2 .

 2 Figure 2. Stressing the processor cores using the STRESS command.

Figure 3 .

 3 Figure 3. The accuracy of POWERAPI when stressing the processor cores.

Figure 4 .

 4 Figure 4. Running a video under MPlayer.

Figure 5 .

 5 Figure 5. Energy consumption of the recursive and iterative implementations of Tower of Hanoi program in C++.

Figure 6 .

 6 Figure 6. Energy consumption of the recursive implementation of Tower of Hanoi program in different languages (using a base 10 logarithmic scale).

http://linux.die.net/man/1/top

Network Integrated Card

formerly Open Service Gateway Initiative

http://sof.tiddlyspot.com

http://www.alciom.com/en/products/powerspy2.html

http://linux.die.net/man/1/stress

http://www.mplayerhq.hu

http://gcc.gnu.org/wiki/PredictiveCommoning

http://www.alertme.com/smart energy

http://www.google.com/powermeter

http://kernel.org/pub/linux/utils/kernel/cpufreq/cpufrequtils.html

http://linux.die.net/man/1/iostat

ACKNOWLEDGMENT This work is partially funded by the French Ministry of Higher Education and Research, Nord-Pas de Calais Regional Council and FEDER through the Contrat de Projets Etat Region Campus Intelligence Ambiante (CPER-CIA) 2007-2013.


Bruno Wilhelm

Fabien Arnaud

Pierre Sabatier

Christian Crouzet

Elodie Brisset

Eric Chaumillon

Jean-Robert Disnar

Frédéric Guiter

Emmanuel Malet

Jean-Louis Reyss

400 years of extreme precipitation patterns over the Mediterranean French Alps and possible forcing mechanisms

Keywords: paleoflood, flood intensity, last millennium, Mediterranean climate, solar activity, North Atlantic Oscillation

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Mountain-river floods triggered by extreme precipitation events can cause substantial human and economic losses [START_REF] Gaume | A compilation of data on European flash floods[END_REF]. Global warming is expected to lead to an increase in the frequency and/or intensity of such events [START_REF] Ipcc | Climate change 2007-the physical science basis[END_REF], especially in the Mediterranean region [START_REF] Giorgi | Climate change projections for the Mediterranean region[END_REF]. However, it is difficult to determine the relationship between such flood events and past climate change because the stochastic nature of extreme events precludes the identification of trends. This problem is compounded by a lack of long-term instrumental data (e.g., [START_REF] Beniston | Future extreme events in European climate: an exploration of regional climate model projections[END_REF]. Consequently, reconstructions of geological records of intense events are an essential tool for extending documentary records beyond existing observational data and thereby building a better understanding of how local and regional flood hazard patterns evolve in response to changes in climate. The results of such studies can be used to improve predictive models of the consequences of global warming (Solomon et al., 2007, and references therein). Lake sediments are continuous archives in which the identification of high-energy sediment layers can be used to reconstruct flood records (e.g., [START_REF] Arnaud | Flood and earthquake disturbance of 210 Pb geochronology (Lake Anterne, North French Alps)[END_REF][START_REF] Moreno | Flood response to rainfall variability during the last 2000 years inferred from the Taravilla Lake record (Central Iberian Range, Spain)[END_REF]) and the thickness of flood deposits can, in some cases, be used as a proxy for flood intensity [START_REF] Irmler | A debris flow calendar derived from sediments of lake Lago di Braies (N. Italy)[END_REF][START_REF] Nesje | A ~4500 years of river floods obtained from a sediment core in Lake Atnsjoen, eastern Norway[END_REF]. Although, changes in vegetation and/or land-use can modify soil stability/erodibility [START_REF] Lanci | A record of Holocene climate in the mineral magnetic record of Alpine lakes: Sägistalsee and Hinterburgsee[END_REF][START_REF] Dapples | Paleolimnological evidence for increased landslide activity due to forest clearing and land-use since 3600 cal BP in the western Swiss Alps[END_REF] and therefore the relationship between single-event terrigenous inputs and flood intensity [START_REF] Giguet-Covex | Changes in erosion patterns during the Holocene in a currently treeless subalpine catchment inferred and from lake sediment geochemistry (Lake Anterne, 2063 m a.s.l., NW French Alps): The role of climate human activities[END_REF], the size of the coarsest sediment fraction still reflects stream flow velocity [START_REF] Francus | Paleoclimate studies of minerogenic sediments using annually resolved textural parameters[END_REF][START_REF] Beierle | A new method for visualizing sediment particle size distributions[END_REF].

Analysis of a Spanish lake sediment archive allowed [START_REF] Moreno | Flood response to rainfall variability during the last 2000 years inferred from the Taravilla Lake record (Central Iberian Range, Spain)[END_REF] to infer that intense precipitation events occurred more frequently during the Little Ice Age (LIA) than they did during the Medieval Warm Period (MWP). These authors also suggested that high flood frequencies were linked to both solar activity maxima and negative North Atlantic Oscillation (NAO) phases. In the northern Italian Alps, the distribution of debris-flow layers in a sequence of lake sediments deposited over the last 2000 yr was interpreted as indicating a high frequency of extreme precipitation events during the LIA [START_REF] Irmler | A debris flow calendar derived from sediments of lake Lago di Braies (N. Italy)[END_REF]. However, the climatic interpretation of debris-flow records is site-dependent and subject to debate (e.g., [START_REF] Jakob | The significance of channel recharge rates for estimating debris-flow magnitude and frequency[END_REF][START_REF] Stoffel | Rainfall characteristics for periglacial debris flows in the Swiss Alps: past incidences-potential future evolutions[END_REF]. The lack of long-term records of extreme precipitation events [START_REF] Luterbacher | Extending North Atlantic Oscillation Reconstructions Back to 1500[END_REF][START_REF] Pauling | Five hundred years of gridded highresolution precipitation reconstructions over Europe and the connection to large-scale circulation[END_REF] is shown by the fact that the above-mentioned studies are the only reconstructions of such events over the last millennium in the northern Mediterranean region, despite the area being particularly sensitive to flood hazards [START_REF] Gaume | A compilation of data on European flash floods[END_REF]. The present study was designed to help redress this situation by constructing a flood record for a site in the Mediterranean French Alps for the last millennium. In order to ascertain the dominant forcing on erosion processes, we first analyzed the sedimentary sequence for signals indicating flood deposits and examined the pollen record to determine the area's land-use history. We then compared the record of paleoflood occurrences at Allos with local historic events and other longterm flood or climatic records. This enabled us to interpret the reconstructed flood signal and to investigate the relationship between extreme precipitation events and climate change in the northwestern Mediterranean region.

Site description and setting

Lake Allos and its tributaries Lake Allos (2230 m a.s.l., 44°14' N, 6°42'35E) is a 1-km-long by 700-m-wide, high-altitude lake in the French Alps. It is located approximately 100 km from the Mediterranean Sea (Fig. 1) and situated in a 5-km²-north-facing cirque, surrounded by sandstone mountains that rise to 2672 m a.s.l. Half of the catchment is drained by three permanent torrents, all of which flow into the lake (Fig. 1 and2).

The Laus torrent drains a regularly sloped sub-catchment (35°) made up of carbonate conglomerates locally covered by several meters of easily erodible glacial till. This stream has built a steep delta where it enters the lake. In contrast, the Lac torrent incises the lakeshore, indicating reduced terrigenous flows toward the lake. The Source torrent drains an area of fractured limestone and enters the lake through a large delta. Hence, the Laus and Source torrents are the dominant sources of sediment entering the lake. Detrital inputs only occur during the summer and the fall, as the catchment is covered by snow and the lake is frozen from November/December to May/June. The lake's outlet, on its northern edge, feeds the Chadoulin River, which stops flowing during exceptionally dry summers. The outlet area is covered by larch forest, whereas the catchment has only very sparse tree cover. Most of the catchment (70%) consists of rock and scree (70%) with alpine meadow vegetation covering most (29%) of the remaining area.

Climate and historic floods

The Mediterranean climate is characterized by quite dry summers and relatively wet falls (Frei andSchär, 1998, Kieffer-Weisse, 1998). Most intense precipitation events occur between June and October/November [START_REF] Kieffer-Weisse | Estimation de paramètres statistiques des précipitations extrêmes dans les Alpes françaises[END_REF], with summer events being the result of local convective phenomena [START_REF] Kieffer-Weisse | Etude des précipitations exceptionnelles de pas de temps court en relief accidenté (Alpes Françaises), Méthode de cartographie des précipitations extrêmes[END_REF][START_REF] Remaître | Morphologie et dynamique des laves torrentielles : Applications aux torrents des Terres Noires du bassin de Barcelonnette (Alpes du Sud)[END_REF] and fall events generally resulting from meso-scale convective systems that affect more extensive areas and that can trigger regional catastrophic floods [START_REF] Buzzi | Mesoscale Meteorological Features Associated with Heavy Precipitation in the Southern Alpine Region[END_REF][START_REF] Boudevillain | Cyclogenèses et précipitations intenses en région méditerranéenne:origines et caractéristiques[END_REF]. Past flood events have been documented from studies of local historical records. This data (stored in a free-to-access database managed by the National Forestry Office and the Mountain Terrain Restoration Service), shows that the village of Allos (5 km downstream from the lake) has been affected by flooding of the Chadoulin River eight times since the end of the 18 th century; in 1788, 1 November 1843, 16 August 1868, in 1886, 23 September 1920, 24 September 1994, 5 November 1994 and 19 June 2005 (see the supplementary material for details on these events).

Material and methods

Seismic survey and coring

We carried out a coupled bathymetric and seismic survey of the sediment infill, using a threetransducer CHIRP sonar (IXSEA, Echoes 5000) from the University of La Rochelle (LIENSs Laboratory) as the seismic source, combined with a short streamer mounted on a dinghy-towed fish.

Chirp length was 10 ms and band-pass frequency was 2 to 8 kHz (vertical resolution ~20 cm). Seismic data processing was carried out using Delph Seismic Software, consisting of gain correction and bandpass filtering. Two-way travel time to depth conversion was based on a P-wave velocity of 1600 m.s -1 [START_REF] Hamilton | Compressional-wave attenuation in marine sediments[END_REF]. Navigation was via a differential GPS system.

In order to constrain the sedimentological behavior of the lake basin, we retrieved thirteen sediment cores using an UWITEC gravity corer. The results presented here are for the three cores that were most representative of flood activity and that covered the longest period. Cores ALO09P12 (44°14'09.6‖N, 6°42'33.6‖E) and ALO09P13 (44°14'10.8‖N, 6°42'33.1‖E) were retrieved from the northern basin, at two different locations in front of the delta, in order to investigate changes in river paths on the delta. Core ALO09P11 (44°14'05.1‖N, 6°42'30.6‖E) was retrieved from the slope between the two basins, in a more distal position with respect to the two deltas. Sediment from this core was used for palynological analysis.

Core description and logging

The cores were split lengthwise and photographed for high-resolution (20 pixels.mm -1 ) visual analysis of lithologies and sedimentary facies.

We carried out grain size analyses on core ALO09P13 (Malvern Mastersizer S), sampling at a continuous interval of 5mm. We then used the median (Q50) and coarsest fractions (Q90) to characterize interbedded deposits [START_REF] Passega | Grain-size representation by CM patterns as a geological tool[END_REF][START_REF] Mulder | Inversely graded turibidite sequences in the deep Mediterranean: a record of deposits from flood-generated turbidity currents?[END_REF].

Geochemical analyses were carried out on the CEREGE laboratory's Itrax™ (Cox Analytical Systems) X-ray fluorescence (XRF) core scanner, using a chromium tube (35 keV, 45 mA) with a 15-s count-time and a 1-mm sampling step. The areas of the element peaks obtained are proportional to the concentrations of each element [START_REF] Tachikawa | The precession phase of hydrological variability in the Western Pacific Warm Pool during the past 400 ka[END_REF]. We quantified total organic carbon contents (TOC) for cores ALO09P11 (1-cm sampling step) and ALO09P12 (2-cm sampling step) using a -Turbo‖ model RE6 pyrolyzer at the ISTO laboratory (see Lafargue et al., 1998, for procedure details).

Pollen analysis

Pollen analyses were carried out at the IMBE laboratory. We chose core ALO09P11 for these analyses, as its distal position with respect to the stream inlets was more favorable to the preservation of pollen grains and spores. Constant volume (4 ml) pollen samples were taken and processed using the method described by [START_REF] Faegri | Textbook of Pollen Analysis[END_REF]. For each sample, we counted at least 300 terrestrial-vascular plantpollen grains,which we identified under the microscope at 500× magnification under oil immersion. We identified and counted 78 taxa but our summary pollen diagram, drawn using C2 software [START_REF] Juggins | C2 Version 1.5 User guide. Software for ecological and palaeoecological data analysis and visualisation[END_REF], only shows selected taxa. We calculated the pollen frequency for each taxon as a percentage of the total pollen. For ecological reasons, we excluded spores from the pollen totals. We obtained the pollen curve for Quercetum mixtum (mixed oak forest) by aggregating percentages for the deciduous genera Quercus, Tilia, Acer, Ulmus and Fraxinus. The Abies/Fagus (beech/fir grove) curve shows total percentages for Abies and Fagus, and the Mediterranean trees curve shows total percentages for Pistacia and Quercus ilex. The -local anthropogenic taxa‖ curve summarizes the dynamics of flora associated with local human activities, that is, ruderal and nitrophilous herbs such as Galium, Rumex, Urtica, Mentha, Plantago lanceolata and Plantago coronopus.

Dating methods

We measured short-lived radionuclides ( 210 Pb, 137 Cs and 241 Am) in the uppermost 16 cm of core ALO09P12, following a non-regular sampling step of 0.5 to 1 cm, in order to match facies boundaries, and using 1-3 g samples of dried sediment. Measurements were made by gamma spectrometry, using high-efficiency, very low background, well-type Ge detectors in the Modane underground laboratory [START_REF] Reyss | Large low background well type detectors for measurements of environmental radioactivity[END_REF]. Generally, counting times of 24 to 48 hr were required to reach a statistical error of less than 10% for excess 210 Pb in the deepest samples and for the 1963 137 Cs and 241 Am peaks.

Excess 210 Pb was calculated as the difference between total 210 Pb and 226 Ra activities. We then used the Constant Flux/Constant Sedimentation (CFCS) model and the decrease in excess 210 Pb to calculate the sedimentation rate [START_REF] Goldberg | Geochronology with lead-210[END_REF]. The uncertainty of the sedimentation rate obtained by this method was derived from the standard error of the linear regression of the CFCS model.

In order to obtain additional chronological markers, we compared the natural remnant magnetization (NRM) versus depth with known secular variations in the geomagnetic field [START_REF] Barletta | Dating of Holocene western Canadian Arctic sediments by matching paleomagnetic secular variation to a geomagnetic field model[END_REF].

Paleomagnetic investigations were performed on u-channel sub-samples of core ALO09P12, using a 3-axis, 2-G enterprise cryogenic magnetometer at the CEREGE laboratory. Because the magnetometer has three orthogonal coils it was possible to use the alternating field demagnetization procedure. Each sample's NRM was progressively demagnetized using (AF) in 10, 20, 30, 40 and 60 mT steps. The demagnetization diagrams [START_REF] Zijderveld | AC demagnetization of rock: analysis of results[END_REF] showed that behavior was mainly unidirectional. In the small number of cases with two overlapping components, the softer component was removed above 30 mT AF. Thus, we used the characteristic remnant magnetization (ChRM) obtained after demagnetization of the NRM 40mT for directional analyses. Samples affected by side effects were removed. We also imparted and demagnetized the samples' isothermal and an hysteretic remnant magnetization in order to ensure there is no change in magnetic mineralogy. Because the magnetic carrier properties were very stable all along the studied section, we could be confident in the resulting NRM direction.

Small-size vegetal macro-remains were sampled at the base of flood deposits. Identifiable pine needles, buds, twigs and leaves were separated and sent for AMS 14 C analysis at France's LMC14 carbon-dating laboratory. 14 C ages were calibrated (Intcal09 calibration curve, [START_REF] Reimer | IntCal09 and Marine09 radiocarbon age calibration curves, 0-50,000 years cal BP[END_REF] Table 1) and an age-model generated using the R-code package -clam‖ [START_REF] Blaauw | Methods and code for ‗classical' age-modelling of radiocarbon sequences[END_REF].

Core ALO09P12 was dated using all the above dating methods, whereas core ALO09P13 was dated via the fine stratigraphic correlation of 156 thick, graded beds, and distal core, ALO09P11, was dated via synchronous geochemical variations.

Results and interpretations

Lake morphology and sediment infill

The lake basin consists of a shallow platform (20 m deep) to the north and a deep basin (42 m deep) to the south (Fig. 1). Seismic data shows the sediment sequence to be well stratified with no evidence of major mass-wasting deposits. The morphology of the acoustic substratum, interpreted as bedrock, indicates flat areas correspond to two distinct sub-basins. Cores ALO09P12 and ALO09P13 were retrieved from the depocenter of the shallow basin, whereas core ALO09P11 was taken on the slope separating the two sub-basins.

Sedimentology

The sediment sequence consists of beds of homogeneous silty clay alternating with coarser, normally graded beds (Figs. 3 and4). All the cores contained two horizons that were greatly enriched in organic carbon and sulfur (Fig. 3). Stratigraphical correlations between the cores based on these horizons showed that ALO09P11 contains the most condensed sediment sequence. Analyses of the interbedded normally graded beds showed a sharp and coarse base overlain by a well-developed fining-upward basal unit and a thin, whitish, fine-grained uppermost unit (Fig. 4). These beds had higher dry bulk densities, lower TOC values and higher calcium contents than the silty clay beds. In addition, the bases of the normally graded beds contained macro-remains of terrestrial vegetation. Normally graded beds are common features of lake sediments, where they are associated with turbidity currents triggered by gravity reworking or flood events [START_REF] Sturm | Turbidites and varves in Lake Brienz (Switzerland): deposition of clastic detritus by density currents[END_REF][START_REF] Arnaud | Flood and earthquake disturbance of 210 Pb geochronology (Lake Anterne, North French Alps)[END_REF][START_REF] Gilbert | The record of an extreme flood in the sediments of montane Lillooet Lake, British Columbia: implications for paleoenvironmental assessment[END_REF]. Because the Lake Allos sequence consists of 160 normally graded beds in less than 2 m of sediment, and because these beds all contained markers indicating an allochtonous origin for the sediment (terrestrial macro-remains and Ca-enrichment), we interpreted them as flood deposits rather than gravity-reworking deposits (e.g., [START_REF] Sturm | Turbidites and varves in Lake Brienz (Switzerland): deposition of clastic detritus by density currents[END_REF][START_REF] Gilbert | The record of an extreme flood in the sediments of montane Lillooet Lake, British Columbia: implications for paleoenvironmental assessment[END_REF]. In addition, 156 of the normally graded beds correlated well between cores ALO09P12 and ALO09P13, but six thin graded beds occurred only in core ALO09P13 (Fig. 3). This suggests that changes in river paths on the delta during flood events occurred very rarely (4% of floods) and that the two cores are representative of the flood activity. Figure 4 compares the thickness and the Q 90max (i.e., the highest Q 90 value of each graded bed) of the 53 beds thicker than 4 mm in core ALO09P13. This comparison could only be done for beds thicker than 4 mm because of the 5-mm sampling step used for grain size measurements. In the present case, the significant positive linear relationship between bed thickness and Q 90max (r=0.88, n=53, p<10 -4 ) allowed us to use bed thickness as a high-resolution proxy for stream velocity during flood events [START_REF] Molinaroli | Relationships between hydrodynamic parameters and grain size in two contrasting transitional environments: The lagoons of Venice and Cabras, Italy[END_REF][START_REF] Parris | Holocene paleostorms identified by particle size signatures in lake sediments from the northeastern United States[END_REF] and therefore as a proxy for flood intensity (e.g., [START_REF] Bøe | Holocene river floods in the upper Glomma catchment, southern Norway: a high-resolution multiproxy record from lacustrine sediments[END_REF][START_REF] Schiefer | A lake sediment-based proxy of floods in the Rocky Mountain Front Ranges, Canada[END_REF].

Chronology

Short-lived radionuclides

The excess 210 Pb profile for core ALO09P12 showed a regular decrease down to a very low value (<15 Bq/kg), punctuated by sharp dips to low values for three layers (2.1-3.3 cm, 10.2-11.3 cm and 13.2-14.9 cm) of thick, graded deposits (Fig. 5A). In line with [START_REF] Arnaud | Flood and earthquake disturbance of 210 Pb geochronology (Lake Anterne, North French Alps)[END_REF], we excluded 210 Pb values associated with instantaneous deposits in order to construct a synthetic sedimentary record (Fig. 5B). Plotting the resulting excess 210 Pb profile on a logarithmic scale revealed two distinct point alignments. Applying the CFCS model [START_REF] Goldberg | Geochronology with lead-210[END_REF] gave mean accumulation rates of 1.12 ± 0.1 mm.yr -1 for the top 8 cm of the core and 0.8 ± 0.1 mm.yr -1 for the lower section of the core. Ages derived using the CFCS mode were applied to the original sediment sequence to give a continuous age-depth relationship (Fig. 5C). In addition, the profiles contained peaks for 241 Am and 137 Cs (Fig. 5A). The 241 Am peak, between 5.1 and 6.1 cm, is characteristic of the decay of 241 Pu in fallout from atmospheric nuclear weapons tests [START_REF] Appleby | 241 Am dating of lake sediments[END_REF]; hence, the maximum 241 Am value can be taken to correspond to the end of nuclear weapon tests in the northern hemisphere, in 1963. The 137 Cs peak can be attributed to fallout from the Chernobyl accident in 1986. The agreement between these independent chronological markers and the 210 Pb ages strongly support our age-depth model for the last century (Fig. 5C).

Age-depth model based on variations in the geomagnetic field and 14 C ages

We used measured and reconstructed declination variations over the last millennium (compilation from [START_REF] Bucur | The direction of the terrestrial magnetic field in France during the last 21 centuries[END_REF][START_REF] Alexandrescu | High-resolution secular variation of the geomagnetic field in western Europe over the last 4 centuries : comparison and integration of historical data from Paris and London[END_REF][START_REF] Gallet | Three millennia of directional variation of the Earth's magnetic field in western Europe as revealed by archeological artefacts[END_REF] to identify magnetic declination minima and maxima. These occurred in AD 1805 ± 15, 1530 ± 65, 1360 ± 15 and 950 ± 40 (D-1 to D-4, respectively, Fig. 6A). The ChRM declination profile of core ALO09P12 also showed four declination features during this period, at depths of 37 ± 1, 56, 82 and 108 cm, allowing the correlation with the reconstructed declination variations (Fig. 6A). These well-correlated declination features provided additional chronological markers that we combined with the short-lived radionuclides results and the 14 C ages (Table 1) to construct an age-depth model. As noted above, we removed flood layers in order to construct a synthetic sedimentary record [START_REF] Bøe | Holocene river floods in the upper Glomma catchment, southern Norway: a high-resolution multiproxy record from lacustrine sediments[END_REF][START_REF] Giguet-Covex | Changes in erosion patterns during the Holocene in a currently treeless subalpine catchment inferred and from lake sediment geochemistry (Lake Anterne, 2063 m a.s.l., NW French Alps): The role of climate human activities[END_REF]. We then calculated an age-depth relationship using a smoothing spline and the -clam‖ R-code package [START_REF] Blaauw | Methods and code for ‗classical' age-modelling of radiocarbon sequences[END_REF]; Fig. 6B). We obtained reliable dates for the graded beds in core ALO09P13 based on the correlation of these beds with those in the dated-core ALO09P12 by using the visual lithological descriptions and the Ca signal (Fig. 3). This allowed us to build a record of Laus torrent floods over the last 1400 yr. For each graded bed, the thickness was attributed to the age (Fig. 8), and we calculated the mean thickness of corresponding flood deposit beds in cores ALO09P12 and ALO09P13, in order to take into account possible changes of river paths. Finally, we determined the age-depth relationship for core ALO09P11 by correlating the two organic-rich, silty clay layers.

Vegetation dynamics

The pollen record is dominated by Pinus type sylvestris (Fig. 7). Pinus pollen is generally overrepresented in pollen records from high-mountain areas because it is widely and massively disseminated by the wind [START_REF] Couteaux | La limite supérieure de la forêt et sa valeur de seuil[END_REF], particularly in open landscapes dominated by grassland.

Consequently, high Pinus pollen values cannot be interpreted as indicating the presence of pine forests close to the lake. These pollen values are more probably due to the presence of scattered stands of pine trees in the upper parts of the valley. The base of core ALO09P11 (Local Pollen Assemblage Zone (LPAZ) 1; Fig. 7) is characterized by the presence of Juglans, low Quercetum mixtum pollen concentrations, a major peak for Poaceae and continuous occurrences of -local anthropogenic taxa‖.

LPAZ 2 is characterized by the first occurrences of pollen from Mediterranean trees, a major peak for arboreal pollen and a continuous curve of Artemisia. There are no Mediterranean trees in the immediate vicinity of the lake, but the substantial quantities of Mediterranean tree pollen in these samples indicate that the landscape surrounding the lake was open. The subsequent zone, LPAZ 3, is characterized by a general decrease in tree pollen (decreasing AP/NAP curve) and high levels of wild grass pollen, such as Poaceae, Cerealia and Ericaceae. This may indicate a significant local and regional destabilization of the mountain ecosystem due to agro-pastoral activities. During LPAZ 4, an increase in the amount of Olea and Juglans pollen is accompanied by a decrease in Quercetum mixtum pollen. This zone also includes a second Poaceae peak, suggesting a continuation of the preceding major disruption in forest dynamics. The most recent zone, LPAZ 5, is characterized by a reduction in non-arboreal pollen and a simultaneous increase in Pinus pollen, as well as the appearance of macroremains of gymnosperms (sub-fossil xylem tracheids). The increase in Pinus pollen and the appearance of gymnosperm macro-remains suggest a recent and progressive recolonization of pine trees from the lower valley to the lake. This is consistent with the current landscape. Overall, the pollen record suggests that the landscape around the lake has been mostly open since LPAZ2, and that agro-pastoral activities occurred throughout the valley during the entire study period.

Discussion

The Lake Allos flood record and its representativeness as a regional climate proxy Once a flood record has been established from proxies, the representativeness of that record has to be determined. Graded beds are generally interpreted as being formed by increases in river discharge, with the high energy features of these beds suggesting that they are the result of flood events, rather than snow melt (e.g., Enters et al., 2010;[START_REF] Giguet-Covex | Frequency and intensity of high-altitude floods over the last 3.5 ka in NW European Alps[END_REF]Wilhelm et al., in press). In the case of Lake Allos, historical sources can be used to determine whether the record merely reflects flooding caused by local meteorological events or whether it can be used as a regional climate proxy.

In addition, it is necessary to assess the effect of land-use changes on erosion patterns, which can be done using pollen data.

The historically well-documented period: local to regional comparison

In order to assess the local reliability of the Allos flood reconstruction, we used the 210 Pbbased ages to determine whether or not the uppermost thick graded beds correlate with documented floods of the Chadoulin River. The most recent graded bed, which only occurred in core ALO09P13, was dated to AD 2005  1 (Fig. 3), suggesting that it corresponds to a local but violent storm that occurred in June 2005. Downcore, the two thick deposits were dated to AD 1995  2 and 1994  2, respectively (Fig. 5C). Both dates and the sediments between these deposits provide a good match with two catastrophic flood events that occurred in September and November 1994. Thick flood deposits lower in the cores were dated to AD 1915 5 and 1881 7, and may correspond to deposits formed by the floods of 1920 and 1886. Other thick flood deposits occurred below the range of the 210 Pb dating method, suggesting that they were formed in the second part of the 19 th century. The dates of AD 1862 9 and 185110 obtained for these deposits by extrapolating the mean sedimentation rate of 0.8 mm.yr -1 are consistent with their formation by the historical floods of August 1868 and 1843.

However, the absence of evidence supporting the assumption of a continuous sedimentation rate below the range of the 210 Pb dating method makes these correlations uncertain. Although there are records of an older flood, in 1788, it would be hazardous to try and correlate this event with a sediment horizon due to the absence of independent chronological controls and the presence of numerous graded beds.

Overall, the good agreement between major historical floods in the last 150 yr and the calculated ages of the thick graded beds supports the interpretation that these beds were deposited by intense flood events. However, some events (e.g., the June 2005 and November 1994 floods) occurred close to times of the year when the lake is usually covered by ice, suggesting that there is a possibility that a flood event may occur when the lake is still ice bound and therefore not be recorded.

To investigate whether or not the Allos flood record is representative of flood events across the region, we compared it with recorded floods for the River Ubaye, approximately 15 km north of Lake Allos, and its main tributaries (Fig. 1), covering periods of 400 and 150 yr, respectively (Remaitre, 2006;Sivan et al., 2009, Fig. 8). This comparison showed that periods of high flood-frequency for the River Ubaye were concomitant with those for its tributaries and at Allos (Figs. 8B). This concordance in flood timing over a range of catchment sizes (from 0.5 to 1000 km²) suggests that most major floods are caused by meso-scale intense precipitation events. In areas with a Mediterranean climate, such meso-scale events most commonly occur in the fall. In contrast, the extreme precipitation rates and torrential floods associated with summer thunderstorms tend to be highly localized (e.g., [START_REF] Merz | A process typology of regional floods[END_REF][START_REF] Sivan | Les conditions climatiques de la torrentialité au cours du Petit Age Glaciaire de Provence[END_REF]Wilhelm et al., in press). These observations led us to interpret the Allos flood record as a signal dominated by regional events. Moreover, the good agreement between different flood records during a cold phase of the LIA [START_REF] Guiot | Growing Season Temperatures in Europe and Climate Forcings Over the Past 1400 Years[END_REF][START_REF] Corona | Longterm summer (AD751-2008) temperature fluctuation in the French Alps based on tree-ring data[END_REF] also suggests that the Allos flood signais are reliable, even if some events are missed during winters when the lake is covered in ice for longer than usual.

The last millennium: potential influence of land-use changes

The climatic information provided by our reconstruction of the Allos flood record for the last few centuries can be considered relevant to longer periods if there have not been any major changes in erosion processes. Changes in erosion processes in high-altitude regions can result from changes in land use and/or soil freeze/thaw due to the presence/absence of permafrost. However, the aspect (west) and altitude (up to ca. 2350 m a.s.l.) of the area eroded by the Laus tributary suggest that the area was unlikely to have been affected by permafrost (e.g., [START_REF] Haeberli | Untersuchungen zur Verbreitung von Parmafrost zwischen Flüelapass und Piz Grialetsch (Graubünden)[END_REF][START_REF] Stoffel | 400 Years of Debris-Flow Activity and Triggering Weather Conditions: Ritigraben, Valais, Switzerland[END_REF]. In addition, an effect of permafrost on erosion processes is incompatible with the high frequency of flood layer formation during the LIA, and the fact that this period also saw the formation of the thickest flood layers. Consequently, our analysis focused uniquely on land use changes. The first period of high flood frequency, around AD 700-800 (Fig. 8), was contemporaneous with major anthropogenic disturbance and with the greatest changes in vegetation dynamics during the study period (Fig. 7).

Hence, the peak in flood frequency may be a result of this change in land use (e.g., [START_REF] Dapples | Paleolimnological evidence for increased landslide activity due to forest clearing and land-use since 3600 cal BP in the western Swiss Alps[END_REF]. Pollen data suggest that human impact on the environment was greatest during the MWP (LPAZ3, Fig. 7), whereas the lowest flood frequency occurred during this period, with only one peak around AD 1140. In contrast, during the LIA there was a general increase in stream activity, albeit with sharp variations. In addition, pollen analyses show that the percentages of anthropogenic taxa and cultivated trees remained almost constant during this period. Except for the period around AD 700, we did not find any correlation between local human activities and the flood signal during the last 1400 yr. Furthermore, the relationship between Q 90max and flood-deposit thickness (Fig. 4) suggests that the amount of eroded material mainly depended on river flow velocity, rather than on the availability of erodible sediment. When soils are destabilized by pastoral land use, the amount of eroded material may become independent of the Q 90 and a secondary pattern would be expected to appear [START_REF] Giguet-Covex | Changes in erosion patterns during the Holocene in a currently treeless subalpine catchment inferred and from lake sediment geochemistry (Lake Anterne, 2063 m a.s.l., NW French Alps): The role of climate human activities[END_REF]. Hence, except during the 8 th century, when land-use changes destabilized the geosystem, the Allos flood record appears to have been mostly driven by climate forcing.

Variations in Allos flood activity and climate change in the northwestern Mediterranean region

Multi-centennial scale

Multi-centennial climate variations during the last millennium can be summarized as an oscillation between -warm and dry‖ periods and -cold and wet‖ periods. Three periods of different duration can be recognized; known as the Medieval Warm Period (MWP), the Little Ice Age (LIA) and the recent warm period. The MWP (AD 800-1300) was characterized by high summer temperatures in southern Europe [START_REF] Guiot | Growing Season Temperatures in Europe and Climate Forcings Over the Past 1400 Years[END_REF], with the exception of a short, cooler and wetter period around AD 1100 [START_REF] Corona | Longterm summer (AD751-2008) temperature fluctuation in the French Alps based on tree-ring data[END_REF]. Reconstructions of lake levels in southern Europe indicate that the MWP included two warmer and much dryer periods, from AD 800 to 1000 and from AD 1150 to 1300 [START_REF] Morellón | Lateglacial and Holocene palaeohydrology in the western Mediterranean region: the Lake Estanya record (NE Spain)[END_REF][START_REF] Morellón | Climate changes and human activities recorded in the sediments of Lake Estanya (NE Spain) during the Medieval Warm Period and Little Ice Age[END_REF], Fig. 8C). In addition, the MWP was marked by very low hydrological activity in large rivers such as the Rhône [START_REF] Arnaud | 7200 years of Rhône river flooding activity in Lake Le Bourget, France: a high-resolution sediment record of NW Alps hydrology[END_REF][START_REF] Debret | North western Alps Holocene paleohydrology recorded by flooding activity in Lake Le Bourget, France[END_REF], Moyenne Durance [START_REF] Miramont | Évolution historique de la morphogenèse et de la dynamique fluviale d'une rivière méditerranéenne : l'exemple de la moyenne Durance (France du sudest)[END_REF] and Tagus [START_REF] Benito | Magnitude and frequency of flooding in the Tagus Basin (Central Spain) over the last millennium[END_REF], and in mountain streams such as the Taravilla lake inlet [START_REF] Moreno | Flood response to rainfall variability during the last 2000 years inferred from the Taravilla Lake record (Central Iberian Range, Spain)[END_REF]. The Allos flood record is consistent with this general dry pattern, although the sharp increase in flood frequency circa AD 1150 appears anomalous, as this was a warm, dry period. However, given the uncertainty of our age model at this depth (i.e., an age error of 50 yr over the last 1000 yr), this increase in flood frequency may correspond to the cold, wet phase circa AD 1100.

The onset of the LIA (ca. AD 1300) was marked by a progressive shift toward cooler and wetter conditions [START_REF] Guiot | Growing Season Temperatures in Europe and Climate Forcings Over the Past 1400 Years[END_REF][START_REF] Corona | Longterm summer (AD751-2008) temperature fluctuation in the French Alps based on tree-ring data[END_REF][START_REF] Morellón | Lateglacial and Holocene palaeohydrology in the western Mediterranean region: the Lake Estanya record (NE Spain)[END_REF], 2011, Fig. 8). Research has shown higher flood activity in large rivers in southern Europe, notably in France [START_REF] Miramont | Évolution historique de la morphogenèse et de la dynamique fluviale d'une rivière méditerranéenne : l'exemple de la moyenne Durance (France du sudest)[END_REF][START_REF] Arnaud | 7200 years of Rhône river flooding activity in Lake Le Bourget, France: a high-resolution sediment record of NW Alps hydrology[END_REF][START_REF] Debret | North western Alps Holocene paleohydrology recorded by flooding activity in Lake Le Bourget, France[END_REF], Italy [START_REF] Belotti | Morpho-sedimentary characteristics and Holocene evolution of the emergent part of the Ombrone River delta (southern Tuscany)[END_REF][START_REF] Giraudi | Late-Holocene alluvial events in the Central Apennines, Italy[END_REF] and Spain [START_REF] Benito | Magnitude and frequency of flooding in the Tagus Basin (Central Spain) over the last millennium[END_REF]in smaller catchments (e.g., in Spain, Moreno et al., 2008). At Allos, the higher flood frequency during the LIA was accompanied by a substantial increase in deposit thickness, which we interpret as being due to an increase in the intensity of extreme rainfall events compared with the MWP (Fig. 8). Thus, the northwestern Mediterranean region was characterized by lower temperatures and wetter conditions, and by more frequent and more intense flood events in the MWP than in the LIA. This pattern is shown by sedimentary records from both Allos and Taravilla. The increased frequency of flood events is consistent with the increased flood activity of large rivers in the northwestern Mediterranean region.

Sub-centennial scale

Although there was an overall multi-centennial increase in the frequency of floods at Allos during the LIA, flood frequencies were highly variable on a sub-centennial timescale. This type of short-term variation has been recognized in a small number of paleohydrological records in the northwestern Mediterranean region. In addition, extensive research using historical documents allowed us to compare the sub-centennial variability in our signal with a detailed flood reconstruction of the Barcelona area [START_REF] Barrera | Estimation of extreme flash flood evolution in Barcelona County from 1351 to 2005[END_REF], as well as a summary of nine flood reconstructions of various rivers along Spain's Mediterranean coast (Barriendos-Vallve and Martin-Wide, 1998, Fig. 8A). First, the timings of periods of high flood frequencies shown by Spanish records, for all sizes of catchment area, are similar to those shown by records for the River Ubaye and its tributaries. In addition, there is good agreement between the flood chronologies for northeast Spain and the southern Alps (i.e., Allos and Ubaye) over the last four centuries, as high flood frequency periods (in the early 17 th century, 1750-1900 and during the late 20 th century) and low flood frequency periods (well-marked around 1700 and in the early 20 th century) are almost synchronous. However, the concordance between flood reconstructions is weaker prior to AD 1600. This change may be due to the intrinsic limits of the reconstructions. For example, the reconstructions for Barcelona drawn up by [START_REF] Barrera | Estimation of extreme flash flood evolution in Barcelona County from 1351 to 2005[END_REF] using historical documents did not reveala consistent flood frequency trend during the first 200 yr. This may be linked to a lack of written sources for this period [START_REF] Barrera | Estimation of extreme flash flood evolution in Barcelona County from 1351 to 2005[END_REF]. This interpretation is supported by the fact that the Lake Taravilla reconstruction indicates two high-flood frequency periods between AD 1400 and 1550 that do not occur in other reconstructions, whereas both Spanish reconstructions present strong similarities since AD 1600. Furthermore, uncertainties in the 14 C dates may affect the overall accuracy of the Taravilla and Allos lake sediment reconstructions.

However, the most intense precipitation events in the northwestern Mediterranean region occur in the Cévennes-Vivarais area (southern France), 250-km west of Allos. Known as -Cévenole‖ events, these episodes are associated with precipitation rates of up to 500 mm/day and are capable of triggering regional high-intensity floods. This type of meso-scale intense precipitation event is a typical feature of Mediterranean climates during the fall [START_REF] Boudevillain | Cyclogenèses et précipitations intenses en région méditerranéenne:origines et caractéristiques[END_REF][START_REF] Gaume | A compilation of data on European flash floods[END_REF]. [START_REF] Blanchemanche | Changement global, effets locaux : Le Petit Age Glaciaire dans le Sud de la France : Impacts morphogéniques et sociétaux[END_REF] used historical documents to draw up a record of autumnal floods for the Rivers Vidourle and Hérault, which are affected by Cévenole events. On comparing this record with our Allos flood reconstruction, we found an inverse correlation between the periods of high flood frequency, measured on a decennial to centennial scale, in the two areas (Fig. 8). This suggests that floods in the southern Alps (i.e., Allos and Ubaye) and the Cévennes-Vivarais region are caused by different meso-scale events. Furthermore, these comparisons showed that flood frequencies were higher in the southern Alps and on Spain's Mediterranean coast than they were in the Cévennes-Vivarais area during the periods AD 1600-1660, 1750-1900 and 1950-2000, and higher in the Cévennes-Vivarais area than in the southern Alps and on the Spanish Mediterranean coast during the periods AD 1680-1750 and 1900-1950. Possible climate mechanisms governing the frequency of torrential floods in the northwestern

Mediterranean region

Major multi-centennial changes in general precipitation patterns during cooling phases, such as the LIA, associated with long periods of reduced solar activity [START_REF] Delaygue | An Antarctic view of Beryllium-10 and solar activity for the past millennium[END_REF] have been recorded in western [START_REF] Magny | Contrasting patterns of hydrological changes in Europe in response to Holocene climate cooling phases[END_REF] and northern Europe [START_REF] Bakke | Strength and spatial patterns of the Holocene wintertime westerlies in the NE Atlantic region[END_REF]. According to [START_REF] Magny | Contrasting patterns of hydrological changes in Europe in response to Holocene climate cooling phases[END_REF], increased moisture over western Europe during cooler periods may result from a southward shift and an intensification of the prevailing westerly winds, due to an increase in the thermal gradient between high and low latitudes. Simulations of mid-latitude cyclonic activity and the resulting precipitation patterns provide support for such mechanisms [START_REF] Trigo | Decline in Mediterranean rainfall caused by weakening of Mediterranean cyclones[END_REF][START_REF] Bengtsson | Storm Tracks and Climate Change[END_REF][START_REF] Raible | Extreme midlatitude cyclones and their implications for precipitation and wind speed extremes in simulations of the Maunder Minimum versus present day conditions[END_REF]. On the other hand, the general increase in hydrological activity at the beginning of the LIA was accompanied by a sub-centennial variability in flood frequency. Most periods of high flood frequency occurred during periods of high solar irradiance [START_REF] Delaygue | An Antarctic view of Beryllium-10 and solar activity for the past millennium[END_REF]Fig. 8), and a relationship between solar maxima and high flood activity has been suggested from flood reconstructions for the River Tagus [START_REF] Vacquero | Solar signal in the number of floods recorded for the Tagus River basin over the last millennium[END_REF] and the Lake Taravilla inlet [START_REF] Moreno | Flood response to rainfall variability during the last 2000 years inferred from the Taravilla Lake record (Central Iberian Range, Spain)[END_REF]. According to [START_REF] Kirov | Long Term Variations and Interrelations of ENSO, NAO and Solar Activity[END_REF], secular solar activity and the NAO are linked: if secular solar activity increases, the -smoothed‖ NAO index decreases, and vice versa. In addition, negative NAO phases are associated with increased precipitation in southern Europe (e.g., [START_REF] Hurrel | Decadal trends in the North Atlantic Oscillation: Regional temperature and precipitations[END_REF][START_REF] Sanchez-Gomez | Intra-seasonal atmospheric variability and extreme precipitation events in the European-Mediterranean region[END_REF]. [START_REF] Luterbacher | Extending North Atlantic Oscillation Reconstructions Back to 1500[END_REF] reported good agreement between periods of high flood frequency and the reconstructed fall NAO index for the last three centuries, with periods of high flood frequency at around AD 1790 and 1880 being associated with highly negative NAO values. However, few floods occurred during the negative NAO period around AD 1680, corresponding to the Maunder solar minimum, although this period has been described as wet (e.g., [START_REF] Pauling | Five hundred years of gridded highresolution precipitation reconstructions over Europe and the connection to large-scale circulation[END_REF]. However, the absence of a marked increase in flood frequency has already been noted in the northwestern Mediterranean region [START_REF] Barriendos | Climatic variations in the Iberian Peninsula during the late Maunder Minimum (AD 1675-1715): an analysis of data from rogation ceremonies[END_REF].

Changes in atmospheric conditions on an multi-annual time scale can also be triggered by major volcanic eruptions, which can cause negative temperature anomalies (e.g., [START_REF] Crowley | Volcanism and the Little Ice Age[END_REF], and either positive (e.g., [START_REF] Blass | Signature of explosive volcanic eruptions in the sediments of a high-altitude Swiss lake[END_REF] or negative [START_REF] Peng | Response of Summer Precipitation over Eastern China to Large Volcanic Eruptions[END_REF] precipitation anomalies.

Comparisons between the paleo-volcanic record [START_REF] Crowley | Volcanism and the Little Ice Age[END_REF] and the Allos record suggest that some flood events may be related to major volcanic activity. However, a consistent correlation supporting volcanism as a main trigger of intense flood event did not appear.

Finally, over the last four centuries there has been a large degree of concordance in the timing of floods in the southern Alps and on Spain's Mediterranean coast. Conversely, there is an inverse relation between flood frequencies in these two regions and those in the Cévennes-Vivarais region.

The meteorological and atmospheric-circulation patterns that led to intense precipitation events in the southern Alps and the Cévennes-Vivarais region between 1994 and 2006 have been closely analyzed [START_REF] Buzzi | Mesoscale Meteorological Features Associated with Heavy Precipitation in the Southern Alpine Region[END_REF][START_REF] Jansa | Western Mediterranean cyclones and heavy rain. Part 1: Numerical experiment concerning the Piedmont flood case[END_REF][START_REF] Ducrocq | A numerical study of three catastrophic precipitating events over southern France. II: Mesoscale triggering and stationarity factors[END_REF][START_REF] Boudevillain | Cyclogenèses et précipitations intenses en région méditerranéenne:origines et caractéristiques[END_REF]Fig. 9).

The results for both areas show that, in the fall, intense precipitation is linked to the still warm Mediterranean Sea moistening and warming the lower atmosphere. During periods of southerly airflow, unstable air masses are carried toward the coast where convection develops, often triggered by the air flowing over the cooler land and being pushed to higher altitudes by the relief. However, on a meso-scale, the direction of the prevailing winds in the lower layers of the atmosphere will depend on the position of the depression center [START_REF] Boudevillain | Cyclogenèses et précipitations intenses en région méditerranéenne:origines et caractéristiques[END_REF]. Meteorological analyses show that Cévenole events occur when the winds blow from the south or southeast, whereas extreme precipitation events in the southern Alps and the Gulf of Genoa occur when the winds are from the southwest [START_REF] Boudevillain | Cyclogenèses et précipitations intenses en région méditerranéenne:origines et caractéristiques[END_REF]Fig. 9). These differences might explain the differences in the reconstructed flood patterns for these two regions. Moreover, the fact that southern Alps-type flood events occur during periods of southwesterly airflow, that is, when the airflow comes from the eastern coast of Spain, suggests that the same meso-scale intense precipitation event might trigger flood events over both eastern Spain and the southern Alps. Combining this observation with reconstructed flood patterns may suggest a 50 to 150-yr oscillation between the two northwestern Mediterranean atmospheric circulation paths that result in intense precipitation events in the area, and that this oscillation has occurred for at least the last 400 yr.

Conclusion

A multi-proxy investigation of the Lake Allos sediment sequence revealed 160 interbedded layers formed over the last 1400 yr. These layers are characterized by a sharp and coarse base, a finingupward trend, high density, low TOC values, the presence of terrestrial vegetal remains and systematic Ca enrichment. The characteristics and frequent occurrence of these deposits suggest that they were formed by high-energy sediment inputs during mountain-torrent floods resulting from extreme precipitation events. In addition, the relationship between the thickness of the graded beds and their basal grain size led us to use bed thickness as a proxy for flood intensity. Comparisons with reconstructions of local historical floods over the last 400 yr suggest that most of these floods were triggered by autumnal, meso-scale intense precipitation events. As there is no evidence for major changes in erosion processes linked to human activities since the MWP, the Lake Allos sediment sequence can be regarded as a record of floods caused by intense precipitation events during the last millennium.

On a multi-centennial time scale, the frequency of Allos flood events is consistent with the general moisture, large-river hydrology and temperature patterns in the northwestern Mediterranean region, that is, low flood activity during the warm/dry MWP and high flood activity during the cold/wet LIA.

Although there was a general increase in flood frequency during the multi-centennial period of the LIA, on a sub-centennial scale flood frequencies were highly variable. This variability may be in phase with solar maxima. In addition, flood frequency peaks appear to be related to autumnal negative NAO phases. Such a relationship has already been reported following paleoflood reconstructions for Spanish rivers [START_REF] Benito | Magnitude and frequency of flooding in the Tagus Basin (Central Spain) over the last millennium[END_REF][START_REF] Moreno | Flood response to rainfall variability during the last 2000 years inferred from the Taravilla Lake record (Central Iberian Range, Spain)[END_REF]. Finally, comparing flood-frequency patterns for different northwestern Mediterranean sites may suggest a 50 to 150-yr oscillation between the two northwestern Mediterranean atmospheric circulation paths that result in intense precipitation events in this region.

Overall, our study shows that sediment sequences from high altitude lakes can provide reliable records of floodfrequency and intensitypatterns related to extreme precipitation events. This opens up the possibility of using such records to investigate the evolution of extreme weather events during longterm periods of climate change. Such information is required to determine the possible impact of the current phase of global warming and therefore better anticipate its impact on societies. [START_REF] Bucur | The direction of the terrestrial magnetic field in France during the last 21 centuries[END_REF][START_REF] Alexandrescu | High-resolution secular variation of the geomagnetic field in western Europe over the last 4 centuries : comparison and integration of historical data from Paris and London[END_REF][START_REF] Gallet | Three millennia of directional variation of the Earth's magnetic field in western Europe as revealed by archeological artefacts[END_REF] and the ChRM declination profile of core ALO09P12 (A). Age-depth model for cores ALO09P12 produced with -clam‖ R-code package, combining historic 137 Cs peaks, 210 Pb resulted sedimentation rate, 14 C ages and magnetic declination features (B). (thickness of flood deposits) with (A) periods of high flood frequency over a large study area and the reconstructions of (C) relative variations in the level of Lake Estanya (Spanish Pyrennees), (D) treering-based summer temperature for the southern Alps and Europe, (E) autumnal NAO index, (F) solar activity and (G) major volcanic eruptions in northern hemisphere. References for the flood records are:

(1) Remaître, 2006, (2) [START_REF] Sivan | Les conditions climatiques de la torrentialité au cours du Petit Age Glaciaire de Provence[END_REF], (3) Blanchemanche, 2009, (4) Barrera et al., 2006, (5) Vallve and Martin-Vide, 1998 based on historical records and (6) [START_REF] Moreno | Flood response to rainfall variability during the last 2000 years inferred from the Taravilla Lake record (Central Iberian Range, Spain)[END_REF] based on a lake sediment sequence. Fig. 9. Dominating situations over the last four centuries during which extreme events affected the dotted area: (1) Allos Lake, (2) Vidourle and Herault Rivers, (3) Rivers of Barcelona county, (4) Rivers of the Spanish Mediterranean coast and (5) Taravilla Lake. The blue arrow and the dashed lines indicate the main moisture flux and its approximate limits, determined from meteorological reanalysis of recent catastrophic -Cévenole‖ and -Southern Alps‖-type events (from [START_REF] Boudevillain | Cyclogenèses et précipitations intenses en région méditerranéenne:origines et caractéristiques[END_REF].

Table . 1. Radiocarbon dates for core ALO09P12 and ALO09P13. We calculated the synthetic sedimentary depth by removing the graded beds, which were considered to be instantaneous deposits.

See text for explanation, nature of samples and calibration procedures.

SUPPLEMENTARY MATERIAL

Table . 2 List of historical flood events on the Chadoulin River, which runs through the village of Allos (based on the National Forestry Service's free-of-access database, see text for details). Reference numbers enable these events to be easily found in the database. N means No, Y means Yes and U, Uncertain.

1

  1400 years of extreme precipitation patterns over the Mediterranean French Alps and possible forcing mechanismsBruno Wilhelm 1 , Fabien Arnaud 1 , Pierre Sabatier 1 , Christian Crouzet 2 , Elodie Brisset 3,4 , Eric Chaumillon 5 , Jean-Robert Disnar 6 , Frederic Guiter 3 , Emmanuel Malet 1 , Jean-Louis Reyss 7 , Kazuyo Tachikawa 4 , Edouard Bard 4 , Jean-Jacques Delannoy 1 (1) EDYTEM, Université de Savoie -CNRS, Pôle Montagne, 73376 Le Bourget du Lac, France (2) ISTerre, Université de Savoie -CNRS, bâtiment Belledonne, 73376 Le Bourget du Lac, France (3) IMBE, Aix-Marseille Université -CNRS, Europole de l'Arbois, BP 80 13545 Aix-en-Provence cedex 04, France (4) CEREGE, Aix-Marseille Université -CNRS, Europole de l'Arbois, BP 80 13545 Aix-en-Provence cedex 04, France (5) LIENSs, Université de La Rochelle -CNRS, Institut du Littoral et de l'Environnement, 2 rue Olympe de Gouges, 17 000 La Rochelle, France (6) ISTO, Université d'Orléans -CNRS, Campus Géosciences, bâtiment ISTE, 1A rue de la Férolerie 45071 ORLEANS cedex 2, France (7) LSCE, Université de Versailles Saint-Quentin CEA-CNRS, avenue de la terrasse, 91198 Gif-sur-Yvette cedex, France e-mail : bruno.wilhelm@univ-savoie.fr tel : 00 33 [0] 4 79 75 87 84 fax : 00 33 [0] 4 79 75 81 71

FiguresFig. 1 .

 1 Figures

Fig. 4 .

 4 Fig.4. Grain size features and Q 90max -thickness relationship for the graded deposits in core ALO09P13

Fig. 6 .

 6 Fig. 6. Correlative magnetic features (D-1 to D-4) between the measured and reconstructed variations

Fig. 7 .

 7 Fig. 7. Pollen diagram for core ALO09P11. Only selected palynological taxa and groups are shown

Fig. 8 .

 8 Fig. 8. Comparison of (B) reconstructed Allos flood frequency (31 yr running average) and intensity

Acknowledgments 21 B. Wilhelm's PhD fellowship was supported by a grant from the Assemblée des Pays de Savoie and the Communauté de Communes du Grésivaudan. Logistical and financial support was provided by the French National Research Agency's Pygmalion project (ANR BLAN07-2_204489). Radiocarbon dating was performed by the national facility LM14C laboratory as part of an INSU ARTEMIS grant proposal. The authors thank Claire Dehlon for her help in identifying vegetal remains, François

Demory for his helpful discussions and for providing access to the CEREGE's paleomagnetism laboratory, Jean-Pascal Dumoulin for his careful processing of our samples for radiocarbon dating, Marta Garcia for her excellent technical advices on using the core scanner, Paul Henderson for the improvement of English, Yoan Laurent for his assistance in processing the seismic data, Marie Revel for her help in the fieldwork and the Mountain Terrain Restoration department of the National Forestry Service for allowing access to its database of historic floods. We also thank Cécile Miramont, an anonymous reviewer, Lothar Schulte and the editors for their constructive comments that greatly improved this manuscript.