Luca Costa

Mario S Rodrigues

Núria Benseny-Cases

Véronique Mayeux

Joël Chevrier

Fabio Comin

Spectroscopic investigation of local mechanical impedance of living cells

The mechanical properties of PC12 living cells have been studied at the nanoscale with a Force Feedback Microscope using two experimental approaches. Firstly, the local mechanical impedance of the cell membrane has been mapped simultaneously to the cell morphology at constant force. As the force of the interaction is gradually increased, we observed the appearance of the sub-membrane cytoskeleton. We shall compare the results obtained with this method with the measurement of other existing techniques. Secondly, a spectroscopic investigation has been performed varying the indentation of the tip in the cell membrane and consequently the force applied on it. In contrast with conventional dynamic atomic force microscopy techniques, here the small oscillation amplitude of the tip is not necessarily imposed at the cantilever first eigenmode. This allows the user to arbitrarily choose the excitation frequency in developing spectroscopic AFM techniques. The mechanical response of the PC12 cell membrane is found to be frequency dependent in the 1 kHz -10 kHz range. The damping coefficient is reproducibly observed to decrease when the excitation frequency is increased. October 24, 2013 1 Introduction:

Atomic Force Microscopes (AFMs) are intensively used for studying cells. Because they provide the morphology of the specimens at the nanoscale, AFMs have been employed for imaging the cell surface and the submembrane cytoskeleton [START_REF] Braet | Comparison of fixed and living liver endothelial cells by atomic force microscopy[END_REF][START_REF] Kasas | A method for anchoring round shaped cells for atomic force microscope imaging[END_REF][START_REF] Grimellec | Imaging of the surface of living cells by low-force contact-mode atomic force microscopy[END_REF]. In addition, AFMs are nowadays extensively used for molecular recognition experiments and to explore the energy landscape of receptor-ligand interactions in living cells [START_REF] Muller | Atomic force microscopy as a multifunctional molecular toolbox in nanobiotechnology[END_REF][START_REF] Muller | Force probing surfaces of living cells to molecular resolution[END_REF]. Another central aspect is the possibility to apply a force on the cell membrane and measure the cell elasticity. The mechanical response of the cells is a key observable for diseases diagnostics [START_REF] Plodinec | The nanomechanical signature of breast cancer[END_REF] and cell signaling [START_REF] Engler | Matrix elasticity directs stem cell lineage specification[END_REF][START_REF] Brown | Cross-correlated tirf/afm reveals asymmetric distribution of force-generating heads along self-assembled, synthetic myosin filaments[END_REF]. More generally, the elasticity is involved in many of the physiological processes performed by the cell. Due to the cell viscoelastic behavior [START_REF] Fabry | Scaling the microrheology of living cells[END_REF][START_REF] Alcaraz | Microrheology of human lung epithelial cells measured by atomic force microscopy[END_REF], the observed mechanical properties could change significantly depending on the frequency probed during an experiment. In this mainframe we focus on the measurement of the mechanical impedance of PC12 living cells employing atomic force microscopy methods.

In conventional static AFMs, the tip is slowly approached close to the cell membrane and a force vs indentation curve is then recorded. Depending on the geometry and the nature of the mechanical contact between the tip and cell, different contact models can be employed to extract the intrinsic elasticity of the cells [START_REF] Rico | Probing mechanical properties of living cells by atomic force microscopy with blunted pyramidal cantilever tips[END_REF][START_REF] Gaboriaud | Atomic force microscopy of microbial cells: Application to nanomechanical properties, surface forces and molecular recognition forces[END_REF]. The approach is generally statistic since a set of approach curves needs to be recorded to properly quantify the cell Young modulus. A two-dimensional force map on the cell may be recorded to get the Young modulus of each cell point [START_REF] Hofmann | Investigating the cytoskeleton of chicken cardiocytes with the atomic force microscope[END_REF][START_REF] Haga | Elasticity mapping of living fibroblasts by afm and immunofluorescence observation of the cytoskeleton[END_REF]. This technique is often time-consuming because a large numbers of force curves needs to be acquired. Recently, advanced AFM operational schemes have been proposed [START_REF] Garcia | The emergence of multifrequency force microscopy[END_REF][START_REF] Raman | Mapping nanomechanical properties of live cells using multi-harmonic atomic force microscopy[END_REF] for measuring nanomechanical properties of soft samples. These methods, called multifrequency AFM, allow the user to simultaneously acquire the topography and the mechanical properties of the specimens. This is possible either monitoring the higher harmonics excited while the tip is interacting with the sample [START_REF] Raman | Mapping nanomechanical properties of live cells using multi-harmonic atomic force microscopy[END_REF], either by exciting and monitoring the 2 nd cantilever eigenmode [START_REF] Martinez-Martin | Noninvasive protein structural flexibility mapping by bimodal dynamic force microscopy[END_REF]. As a consequence, the sample elasticity is probed at frequencies linked to the cantilever eigenmodes. These techniques are much faster than the conventional force mapping.

We have introduced an alternative operational scheme based on fiber optic detection system, called Force Feedback Microscope (FFM) [START_REF] Rodrigues | Why do atomic force microscopy force curves still exhibit jump to contact?[END_REF][START_REF] Costa | Imaging material properties of biological samples with a force feedback microscope[END_REF]. It allows the user to simultaneously measure the static force, the elastic force gradient and the damping coefficient, fully characterizing the interaction between the AFM probe and the specimen. The static force is the output of an active feedback loop that controls the average position of the tip in the space. A sub-nanometric oscillation amplitude is then imposed to the tip for measuring the force gradient and the damping coefficient. The use of small oscillation amplitudes indicates the Force Feedback Microscope to be essentially a linear AFM. At the solid/liquid interface soft cantilevers with a stiffness in the order of 0.01 N/m are employed. The use of soft cantilevers and small oscillation amplitude is essential to reduce the invasiveness of the AFM experiment [START_REF] Guzman | Peak forces in high-resolution imaging of soft matter in liquid[END_REF] when soft samples have to be studied. A central aspect of the Force Feedback Microscope is the capability to use as feedback signal to record the sample morphology either the force, either the force gradient, either the damping coefficient. One out of these three different quantities can be arbitrarily kept constant, providing a contrast in the other two physical observables. In these conditions, the contrast is intrinsically dependent on the choice of the feedback signal, the magnitude of the setpoint, the nature of the interaction and the local change of the interaction on the specimens [START_REF] Costa | Imaging material properties of biological samples with a force feedback microscope[END_REF]. An additional key point of the Force Feedback Microscope (FFM) is the possibility to arbitrarily choose the excitation frequency imposed to the cantilever. This capability is here exploited to characterize the mechanical response of PC12 living cells in the 1 kHz -10 kHz frequency range. For this purpose, PC12 living cells have been previously characterized in conventional static mode. Subsequently, cells have been imaged at different constant forces in the FFM mode. The topography is acquired simultaneously to the measured sample stiffness and the damping coefficient. Finally, a set of indentation curves at different excitation frequencies has been recorded on the top of the PC12 living cells.

Materials and methods:

Cell line PC12 were obtained from ATCC. PC12 is a cell line derived from a pheochromocytoma of the rat adrenal medulla and is extensively used as a neuronal model. In the experimental conditions used the cells were not differentiated.

Cell culture

Some existing protocols have been followed ( [START_REF] Viro | Rapid inhibition of ca2+ influx by neurosteroids in murine embryonic sensory neurones[END_REF][START_REF] André | Axotomy-induced expression of calciumactivated chloride current in subpopulations of mouse dorsal root ganglion neurons[END_REF]). The PC12 cells are cultivated inside 250 ml Flasks produced by BD Falcon. The cell medium is a solution of Minimum Essential Medium, with 10% Fetal Bovine Serum, supplemented with antibiotics 1% Penicillin-Streptomycin at 100 ng/ml. All the products are produced by Life technologies. The medium is changed every three days. Cells are cultivated inside an incubator at 37 • C, 5 %CO 2 .

Glass support for AFM measurements

The glass coverslips (Thermanox, 13 mm diameter) are previously autoclave-ted for sterilization. The coverslips are deposited inside the 4 wells Culture plates (Thermo Scientific Nunc dishes IVF ), steryle. 500 µl of Fibronectin 1 µg ml (Life technologies) are deposited in each culture plate and the sample is then left incubated for 60 minutes at room temperature. The Fibronectin is then removed and each culture plate is washed 4 times with sterile PBS. 500 µl of MEM with 10% Fetal Bovine Serum and 1% Penicillin-Streptomycin are deposited in each culture plate.

Cells transfer on the glass support for the AFM measurements:

The buffer inside the flask is removed. A solution of 15 ml PBS, 0.25% Trypsin at 1 mg/ml is transferred inside the flask. The flask is then left incubated for 30 minutes at 37 • C. The cells are now suspended in the solution. 500 µl of fetal bovine serum are added to the solution. The solution is centrifuged for 5 minutes at 12500 rpm inside a 15 ml centrifuge tube. The solution is removed leaving the cells at the bottom of the tube. 2 ml of MEM, 10% fetal bovine serum, 1% Penicillin-Streptomycin at 100 ng/ml are inserted in the tube. Cells are mechanically re-suspended using a Pasteur pipette. The cells concentration is then calculated using a Neubauer cell. Cells have been usually deposited in each culture plate on the top of the coverslip glass at a concentration of 1 million/ml. The cells are then left in the incubator at 37 • C, 5 %CO 2 for one/two nights and then measured with the FFM. The imaging buffer is MEM, 1% Penicillin-Streptomycin at 100 ng/ml.

Force Feedback Microscopy

The Force Feedback Microscope is a custom -made AFM based on a fiber optic interferometer detection system [START_REF] Rodrigues | Why do atomic force microscopy force curves still exhibit jump to contact?[END_REF]. imaging buffer during the measurements. A central aspect of the Force Feedback Microscope is the calibration of the instrument. For this purpose, a set of approach curves between the tip and the glass has been recorded for each excitation frequency ω.

∇F = a [cos(φ ∞ ) -n cos(φ)] (1)
γ = a ω [sin(φ ∞ ) -n sin(φ)] (2)
Equation ( 1) and (2) convert the measured values n and φ into the interaction properties ∇F and γ. φ is the phase between the oscillation of the tip and excitation at the cantilever base, whereas n is the so-called normalized amplitude. Equation ( 1) and ( 2) can be used mainly because the oscillation amplitude imposed to the tip is kept below 1 nm. It follows that the force feedback microscope can be considered as a linear AFM.

In this case, assuming the integrated force gradient to be equal to the measurement of the static force between the AFM probe and the glass, the key point is to determine the parameters φ ∞ and a. The protocol of calibration is explained in details elsewhere [START_REF] Rodrigues | Why do atomic force microscopy force curves still exhibit jump to contact?[END_REF]. In figure 1 the elastic force gradient and the damping coefficient measured on the glass at 4 different excitation frequencies are reported. The elastic force gradient is found to be independent of the frequency probed (figure 1a). The damping coefficient is observed to decrease at larger excitation frequencies (figure 1b). Since the dissipative force is proportional to the damping coefficient times the speed of the tip, we do not observe a significant decrease in the force needed to maintain the tip oscillations constant when the excitation frequency is increased. In other words, the dissipative force exerted by the glass on the AFM tip is found to be constant in the 1 kHz -10 kHz range. 3 Results:

Characterization in conventional static mode

Cells have been firstly characterized with conventional AFM static mode. In figure 2 a typical image acquired at a constant repulsive force of 100 pN is reported. Since in our custom -made AFM it is not possible to laterally offset the sample from the tip, a scan area of 100 x 100 µm 2 is the only sample surface accessible to obtain an image. As a consequence, the cell concentration employed for the cell culture has been refined in order to obtain a cell density on the glass support similar to the one presented in the image. Indeed, the presence of the glass support in the scan area is a compulsory condition to calibrate the FFM.

Once the cells are successfully imaged, a set of indentation force curves has been acquired on the living cells. The tip can be modeled as a four-sided pyramidal indenter. In this case, a relationship between the force F and the indentation δ [START_REF] Bilodeau | Regular pyramid punch problem[END_REF] can be given by

F = 3Etanθ 4(1 -ν 2 ) δ 2 (3)
where E is the Young modulus and ν is the Poisson ratio of the cell. ν is assumed to be equal to 0.5. The nominal θ of the probes supplied with the MLCT cantilevers is 25 • . In figure 3 a typical force vs indentation curve is shown. The measured Young modulus is 1716 Pa which is comparable to the one measured on PC12 living cells in previous works [START_REF] Chang | morphology and ultrastructure-related local mechanical properties of pc-12 cells studied by integrating atomic force microscopy and immunofluorescence imaging[END_REF].

Characterization at constant force in force feedback mode

Cells have been imaged in Force Feedback Microscopy mode at a constant repulsive force of 500 pN. In this imaging mode, the force is kept constant during the scan and the topography is acquired simultaneously to the force gradient and the damping factor. An amplitude of 0.4 nm has been imposed to the tip at 2.2 kHz. The Force Feedback Microscope has been then calibrated [START_REF] Rodrigues | Why do atomic force microscopy force curves still exhibit jump to contact?[END_REF] with a set of approach In the images at 1 nN (e,f,g,h) the scale bar is 90 µm. e) Sample topography, f) force (error), g) force gradient and h) damping factor.

curves performed on the glass. Subsequently, the tip has been brought in contact with the sample at a constant repulsive force of 500 pN and the image has been acquired. In figure 4a is reported the topography and in figure 4b the force between the tip and the sample which is the error signal. In addition, 4c is the measurement of the force gradient, whereas 4d is the damping factor. The membrane is found to be softer in the center of the cell than at the periphery. In analogy with the stiffness, the damping factor is observed to be lower in the center of the cell than at the borders of the cell. In fibroblasts, these effects can be due to the conformation of the cytoskeleton, in particular of the actin filaments and the microtubules inside the cell as suggested by [START_REF] Haga | Elasticity mapping of living fibroblasts by afm and immunofluorescence observation of the cytoskeleton[END_REF][START_REF] Rotsch | Drug-induced changes of cytoskeletal structure and mechanics in fibroblasts: An atomic force microscopy study[END_REF][START_REF] Li | Estimating microtubule distributions from 2d immunofluorescence microscopy images reveals differences among human cultured cell lines[END_REF]. These effects can be also due to the higher influence of the glass substrate when the measurements of the stiffness and the damping factor are carried out at the periphery of the cells. The measured stiffness of the cells is comparable to the stiffness measured in multi-harmonic atomic force microscopy on different cells [START_REF] Raman | Mapping nanomechanical properties of live cells using multi-harmonic atomic force microscopy[END_REF].

In a different experiment, PC12 cells have been imaged at the constant force of 1 nN. An amplitude of 0.4 nm has been imposed to the tip at 7.78 kHz. In figure 4e is reported the topography, in figure 4f the force (error signal), in figure 4g the elasticity and in figure 4h the damping coefficient. Figure 4e reveals the presence of actin filaments/microtubules which are however hardly measured in the elasticity and damping coefficient images. In analogy with the measurement at constant force of 500 pN, stiffness and damping factor are observed to be lower in the center of the cells than at the periphery of the cells.

Characterization at constant force and tunable frequency

The capability of the FFM to arbitrarily choose the excitation frequency of the AFM tip allows the user to measure the sample force gradient and the sample damping factor at the desirable frequency. For this purpose a set of calibration curves on the top of the glass substrate have been performed at 2.25 kHz and then at 13.25 kHz. The oscillation amplitude imposed to the tip is 0.4 nm. Once the FFM is calibrated, one image at constant repulsive force of 50 pN has been acquired at the excitation frequency of 2.25 kHz (figure 5a,b,c,d). Consequently, the same scan area has been imaged a second time at constant repulsive force of 50 pN, imposing an excitation frequency of 13.25 kHz (figure 5e,f,g,h). In analogy with the measurement presented in figure 4, the cell center is again observed to be softer than the cell borders.

The stiffness and the damping factor measured at 2.25 kHz are nosier than those measured at 13.25 kHz mainly because of the larger influence of the noise 1 f . In order to run an acceptable measurement at low frequency, the scan speed at 2.25 kHz has been set twice slower than the one at 13.25 kHz.

The changes in the sample morphology between figure 5a and figure 5e are due to the fact that the cells are alive. We observe consistent differences between the images of the elasticity and the damping factor. The cells are found to be stiffer at 13.25 kHz than at 2.25 kHz by a factor five. The damping factor is instead observed to be larger at 2.25 kHz than at 13.25 kHz. The decrease of the damping factor once the excitation frequency is increased is a behavior observed on the spectroscopy performed on the glass (figure 1).

Indentation curves

A spectroscopic investigation of the PC12 cells has been developed through the acquisition of tip-cell approach curves. The protocol is the following: • The Force Feedback Microscope was calibrated with a set of approach curves at different frequencies on the glass. A set of parameters (φ inf ty ) i and a i for each excitation frequency ω i were obtained. The force gradients ∇F i are equal for each excitation frequency as shown in figure 1a.

• A set of approach curves at different frequency on the PC12 was performed in the highest point of the cell topography.

For an applied force of 500 pN, the observed cell elasticity in all the measurements has always been found in the order of 0.01 N/m. An example of such an experiment is presented in figure 6 We notice that the damping rate is decreasing when the excitation frequency increases, whereas the force gradient is increasing with the frequency. The increase of the elasticity with the frequency has not been observed to be reproducible in all the measurements performed. In particular, we observe a decrease of the cell elasticity at frequencies larger than 13 kHz. The decrease of the damping factor with the frequency is, on the contrary, reproducible. The resulting dissipative force, presented in the inset of figure 6b, is observed to be poorly dependent on the frequency. According to [START_REF] Alcaraz | Microrheology of human lung epithelial cells measured by atomic force microscopy[END_REF], the damping factor is expected to increase with the excitation frequency in the 1 Hz -100

Hz range. However, in our case the oscillation amplitude imposed to the tip is 2 orders of magnitude lower than the usual amplitudes used for studying living cells. Since in the present case the oscillation amplitude is much smaller than the cell thickness, we think that the effect of of what is below the membrane may be smaller when compared to the case were the amplitude of oscillation is larger than to the cell thickness. In addition, a small oscillation amplitude may induce measurements of stiffness and damping factor which are more localized on the cell membrane than in an experiment where larger oscillation amplitudes are imposed to the tip.

Analysis of elastic properties in dynamic mode:

Focusing on the elasticity, from equation ( 3) we can define the force gradient as a function of the tip indentation as

∇F = 3Etanθ 2(1 -ν 2 ) δ (4)
In particular, the data presented in figure 6a In the last decades, the Force Modulation technique [START_REF] Radmacher | Imaging viscoelasticity by force modulation with the atomic force microscope[END_REF] has been applied to the study of the variation of the elasticity of the cells, typically at frequencies lower than 100 Hz [START_REF] Alcaraz | Microrheology of human lung epithelial cells measured by atomic force microscopy[END_REF][START_REF] Luque | Local micromechanical properties of decellularized lung scaffolds measured with atomic force microscopy[END_REF]. In this frequency range the studied cells show an increase of the young modulus with frequency following specific power laws.

Here the study has been extended to frequencies larger than than 1 kHz. As reported in the previous section, no clear reproducibility of the increase of the cell stiffness has been measured. However, the observed cell elasticity in all the measurements performed has always been found in the order of ten(s) of kPa in this frequency range. The measured Young modula are therefore observed to be between one and two orders of magnitude larger than the static values (figure 3).

Spatial variation analysis of elastic properties:

The spatial variation of the cell elasticity can be studied with the FFM during the acquisition of the cell morphology as presented in figure 4 and figure 5. In analogy with the study of the cell elasticity in dynamic mode, it is possible to extract the spatial variation of the Young modulus. The simultaneous measurement of the elasticity and the applied force allows us to evaluate the Young modulus of the cells through the relation 5.

(∇F ) 2 F ≈ 3Etan(θ) 1 -ν 2 (5)
In figure 8 the extracted Young modulus of PC12 living cells imaged at a constant repulsive force of 1 nN is reported.

Figure 8: Spatial variation of the Young modulus of PC12 living cells imaged at constant repulsive force of 1 nN and at the excitation frequency of 7.78 kHz. The Young modulus is extracted using relation [START_REF] Muller | Force probing surfaces of living cells to molecular resolution[END_REF].

The scale-bar of figure 8 is limited to 3 MPa to enhance the contrast in the spatial variation of the cell Young modulus. The periphery of the cells is observed to be much stiffer than the center, reaching values larger than 1 MPa. The Young modulus in the center of the cell is observed to vary from 50 kPa up to 400 kPa. The extracted Young modulus may be compared with the values estimated on different living cells in multi-harmonics AFM [START_REF] Raman | Mapping nanomechanical properties of live cells using multi-harmonic atomic force microscopy[END_REF], revealing a consistent agreement.

Conclusion:

Concluding, the Force Feedback Microscope measures simultaneously the force, the force gradient and the damping factor at different excitation frequencies, fully characterizing the interaction between the AFM tip and the sample. We developed two spectroscopic protocols to study living cells with a Force Feedback Microscope. The techniques have been applied to the study of PC12 living cells. At first, cells have been characterized with images at different constant forces and at different excitation frequencies. Then, the response of the cell membrane has been characterized through the acquisition of approach curves at different frequencies. The local mechanical impedance, that is the elasticity and the damping factor, have been measured and characterized in the 1 kHz -10 kHz range. Our measurements have been compared with data existing in literature revealing that the amplitude of oscillation applied on the tip is a crucial parameter when the cell elasticity and dissipation have to be characterized. The local mechanical impedance of the PC12 living cell has been characterized in the x/y plane simultaneously to the cell morphology in analogy with already existing technique such as multiharmonics AFM [START_REF] Raman | Mapping nanomechanical properties of live cells using multi-harmonic atomic force microscopy[END_REF]. Finally, it is clear that one of the future objectives is the extension of the lower and upper limits of the available excitation frequencies, 1 kHz and 15 kHz respectively.

Figure 1 :

 1 Figure 1: Spectroscopy on the glass in liquid buffer. Force gradient (a) and damping factor γ (b) as a function of the excitation frequency. Excitation frequencies: Blue = 1.11 kHz, Red = 3.11 kHz, Green = 9.11 kHz, Black = 11.11 kHz.

  Triangular and rectangular MLCT cantilevers provided by Bruker with nominal spring constants of 0.01 N/m and 0.02 N/m respectively have been employed. Both the cantilevers have a nominal tip radius of 20 nm. An Asylum Research fluid cell has been modified to fit properly in the home made Force feedback Microscope. The glass coverslips have been fixed inside the liquid cell. Cells have been kept at 37 • C controlling the temperature of the

Figure 2 :

 2 Figure 2: PC12 living cells imaged in conventional contact mode. Scan area = 90 x 90 µm 2 . Several cells of different shapes are here imaged.

Figure 3 :

 3 Figure 3: Indentation static curve on a PC12 (blue) fitted with Hertz model(red). The tip is modeled as a foursided pyramidal indenter. The Young's modulus extracted is 1716 Pa using a Poisson ratio equal to 0.5 and θ = 25 • .

Figure 4 :

 4 Figure 4: FFM images of PC12 living cells acquired at a constant force of 500 pN and 1 nN. In the images at 500 pN constant force (a,b,c,d) the scale bar is 30 µm. a) Sample topography, b) force (error), c) force gradient and d) damping factor.In the images at 1 nN (e,f,g,h) the scale bar is 90 µm. e) Sample topography, f) force (error), g) force gradient and h) damping factor.

Figure 5 :

 5 Figure 5: FFM images of PC12 living cells acquired at a constant force of 50 pN. Scan area = 90 x 90 µm 2 . a) Sample topography, b) force (error), c) force gradient and d) damping factor at 2.25 kHz. e) Sample topography, f) force (error), g) force gradient and h) damping factor at 13.25 kHz

Figure 6 :

 6 Figure 6: Spectroscopy of the PC12 in liquid buffer. Force gradient (a), damping factor (b) and dissipative force (inset) as a function of the excitation frequency. Excitation frequencies: Blue = 1.13 kHz, Red = 5.13 kHz, Green = 7.13 kHz, Black = 11.13 kHz.

  have been used to extract the cell Young modulus as a function of the excitation frequency. The Young modula are found to be equal to 8690 Pa at 1.13 kHz, 18604 Pa at 5.13 kHz, 26385 Pa at 7.13 kHz and 27023 Pa at 11.13 kHz. The data are reported in figure 7.

Figure 7 :

 7 Figure 7: Analysis of the PC12 elastic properties through indentations curves. a) Force gradient as a function of the tip indentation. a) ω = 1.13 kHz, b) ω = 5.13 kHz, c) ω = 7.13 kHz, d) ω = 11.13 kHz. The lines in orange are the experimental linear fit of the cell elasticity using equation (4).

ACKNOWLEDGMENTS

Luca Costa acknowledges COST Action TD 1002. Mario S. Rodrigues acknowledges financial support from Fundação para a Ciência e Tecnologia SFRH/BPD/69201/2010. This work was performed at the Surface Science Laboratory of the ESRF and at the PSB laboratories in the Carl-Ivar Brändén building in Grenoble.


Tina Comes
email: tina.comes@uia.no

Brice Mayag
email: brice.mayag@dauphine.fr

Elsa Negre
email: elsa.negre@dauphine.fr

Early Warning and Decision Support Systems

Keywords: Early warning systems, decision support, vulnerability assessment, indicator framework, Typhoon Haiyan, disaster response

Warnings can help prevent damage and harm if they are issued timely and provide information that help responders and population to adequately prepare for the disaster to come. Today, there are many indicator and sensor systems that are designed to reduce disaster risks, or issue early warnings. In this paper we analyze the different systems in the light of the initial decisions that need to be made in the response to sudden onset disasters. We outline challenges of current practices and methods, and provide an agenda for future research.

To illustrate our approach, we present a case study of Typhoon Haiyan. Although meteorological services had issued warnings; relief goods were prepositioned; and responders predeployed, the delivery of aid was delayed in some of the worst hit regions. We argue for an integrated consideration of preparedness and response to provide adequate thresholds for early warning systems that focus on decision-makers needs.

INTRODUCTION

Timely information has been described as key to disaster response (IFRC 2014, Kamissoko et al. 2014,b). Understanding and adequately reacting to early warning signs, before these become manifest and turn into acute needs is in many cases more effective and efficient than responding only after a disaster hit [START_REF] Swithern | Global Humanitarian Assitance Report[END_REF]. Ideally, early warning signals should trigger appropriate actions to prevent harm from the population, such as evacuation, or to get ready to respond, such as pre-positioning of goods and deployment of responders Today, the world is suffering from violence and conflict; resource scarcity; epidemics; and natural hazards such as floods, storms, or earthquakes. Depending on the nature of the disaster, signals, and how to interpret them as BACKGROUND Warnings, and the decisions to evacuate the population; deploy disaster relief teams into a region; or preposition goods, are the interface between preparedness and response. Figure 1 shows that the earlier a warning is, the more time there is to plan and coordinate preventive activities. However, the information about the hazard becomes more accurate as time passes. A typhoon typically starts as a precipitation over sea. As it evolves and approaches land, its path and magnitude can be better predicted, enabling more precise forecasts of damages and needs. Additionally, the threat becomes more concrete and the willingness to act and comply with preventive measures rise, which disrupt day-to-day life and business. Decision-makers need to carefully balance the timing of early warning decisions by taking into account  Intervention points: what are possible actions? How much time is required to perform each action?

How much time until disaster will strike?

 Accuracy and precision of information about the disaster: its magnitude, path, and consequences

Long Paper -Decision Support Systems Proceedings of the ISCRAM 2015 Palen, Büscher, Comes & Hughes, eds.

Figure 1. The Context of Early Warning Decisions

EWS are combine the functions of monitoring, decision-making and communication. To this end, they combine communication hardware that must be reliable and robust, especially during the natural disasters; with a communication platform. Ideally, the latter enables interactions among decision-makers, affected population, experts predicting the magnitude and consequences of the disaster, and responders, turning. Communication technology into a key element in early warning [START_REF] Ewc Ii | Report on Integrating Early Warning into Relevant Policies[END_REF][START_REF] Grasso | Early Warning Systems: State-of-Art Analysis and Future Directions[END_REF]. Since in sudden onset disasters time is short, messages are often simplified and reduced to a simple "Red Alert", instead of providing concrete advice and guidance on the next steps or establishing a platform for deliberation.

Vulnerability Assessments

The impact of a disaster is worst, where it hits vulnerable population and regions, or critical infrastructures and economic sectors. Vulnerability describes the susceptibility of a society or system to damages from a hazard event [START_REF] Bonapace | Reducing vulnerability and exposure to disasters: Asia-Pacific disaster report 2012[END_REF][START_REF] Cardona | The Need for Rethinking the Concepts of Vulnerability and Risk from a Holistic Perspective: A Necessary Review and Criticism for Effective Risk Management[END_REF][START_REF] Un/Isdr | Indicators of progress: guidance on measuring the reduction of disaster risks and the implementation of the Hyogo Framework for Action. Geneva, United Nations secretariat of the International Strategy for Disaster Reduction[END_REF]. In supply chain management processes are critical, if they are vital for a supply chain's success [START_REF] Craighead | The Severity of Supply Chain Disruptions: Design Characteristics and Mitigation Capabilities[END_REF][START_REF] Klibi | The design of robust value-creating supply chain networks: A critical review[END_REF]. Here, we define infrastructures or services as critical if their functioning is essential for the society, for instance in terms of protection of the population; environmental targets; or longer term economic development.

Vulnerability and criticality assessments can help focus early warning decisions. Depending on the nature of aims, different facets can be emphasized: vulnerability can focus on specific socio-demographic aspects, such as age, or health [START_REF] Adger | Vulnerability[END_REF][START_REF] Weichselgartner | Barriers in the science-policy-practice interface: Toward a knowledge-action-system in global environmental change research[END_REF]; economic development [START_REF] Cannon | Vulnerability, resilience and development discourses in context of climate change[END_REF][START_REF] Ingram | Post-disaster recovery dilemmas: challenges in balancing short-term and long-term needs for vulnerability reduction[END_REF]Merz, Hiete, Comes, & Schultmann, 2012); or (critical) infrastructures [START_REF] Egan | Anticipating Future Vulnerability: Defining Characteristics of Increasingly Critical Infrastructure-like Systems[END_REF][START_REF] Wang | Vulnerability analysis of interdependent infrastructure systems: A methodological framework[END_REF]. Other branches address the vulnerability of geographical regions against specific hazards, often focusing on protection of coastal regions against floods or storms (Adger, 1999;[START_REF] Grünthal | Comparative Risk Assessments for the City of Cologne --Storms, Floods, Earthquakes[END_REF][START_REF] Judge | Vulnerability Indicators for Coastal Dunes[END_REF][START_REF] Torresan | Assessing coastal vulnerability to climate change: comparing segmentation at global and regional scales[END_REF].

Although vulnerabilities are typically measured on cardinal scales, the values are used to establish ordinal vulnerability rankings that enables decision-makers to prioritize regions, sectors or communities that require most attention and protection. Yet, to our best knowledge there are currently no systems that explicitly take into account planned interventions and actions in the acute phase of a disaster or emergency when setting up the vulnerability assessments and scales.

Early-Warning Systems (EWS)

Early-warning is the provision of timely and effective information that allows organizations and individuals to take action to avoid or reduce their risk and prepare for effective response (Hyogo, 2005). Although EWS are specific to the context, some general principles have been defined by [START_REF] Glantz | Early Warning Systems: Do's and Don'ts. Usable science[END_REF]: continuity in operations, timely warnings, transparency, integration, human capacity, flexibility, and neutralitypartially matching the humanitarian principles humanity, neutrality, impartiality and independence. According to the UN (2006) and the Public Entity Risk Institute (2010), a complete and effective EWS comprises four elements: risk knowledge; monitoring and warning service; dissemination and communication; and response capability. Failure of any part of the system will imply failure of the whole system. Since we are interested in the design of EWS, we focus on risk knowledge, which takes into account prior knowledge of the risks and planning of the control system in terms of sensors, measures, scales, and thresholds.

To issue precise and adequate warnings, EWS need to combine current and local information with knowledge from past events, static structures, and trends [START_REF] Schrodt | The impact of early warning on institutional responses to complex humanitarian crises[END_REF][START_REF] Mea | Ecosystems and human well-being: A framework for assessment[END_REF]. In order to provide relevant and actionable information EWS need to be user-centred, interoperable and facilitate interactions between decision-makers, and stakeholders [START_REF] Hall | Early warning systems: Reframing the discussion[END_REF]Hyogo, 2005). In an era that is characterised by international collaborations [START_REF] Engel | Early warning systems: A review[END_REF] respecting the different requirements and information sharing protocols of the involved organizations and actors has become more challenging than ever before [START_REF] Swithern | Global Humanitarian Assitance Report[END_REF]. Hence, there is a need for transparent and structured processes that support the design of EWS and take into account -Societal and organizational preferences, requirements, and protocols; -Vulnerabilities and criticalities of potentially affected regions; Characteristics of potential mitigation measures and intervention windows.

Long Paper -Decision Support Systems Proceedings of the ISCRAM 2015 Palen, Büscher, Comes & Hughes, eds.

MEASURING THE FUTURE -TYPHOON INDICATOR SYSTEMS

Indicators help decision-makers turn abstract concepts such as vulnerability into concrete guidance on where to focus attention and resources. Yet, there is no consensus on which indicators should be used for which purpose, and how the values of indicators as diverse as Atmospheric Moisture and Corruption Level should be combined to provide support disaster responders.

Indicators and Thresholds

An indicator is a decision support tool that helps decision-makers breaking complex problems down into small and measurable portions, focusing on the most important aspects of a situation. Indicators can be qualitative or quantitative; some indicators are themselves aggregate indices. The usefulness of an indicator depends primarily on its ability to reflect the intended aspect of reality, but also its ease of acquisition and understanding. An effective indicator must satisfy several criteria: To be operational, an indicator is delivered with a simple rule to interpret its meaning. One of the most well known indicators is certainly climate change, where there are fierce debates about to which threshold global warming needs to be limited, to avoid uncontrollable damage. For a guideline on how to derive such threshold for meteorological phenomena, see (Guzetti et al. 2007). Knowledge and experience about the meaning and significance of the indicator, and its values or its interplay with other indicators need to be available to determine a threshold, corresponding to the limit between satisfactory and unsatisfactory or acceptable and unacceptable conditions.

 Robust,

Vulnerability Indicator Systems

Owing to the abstract nature and ambiguity of the concept, indicators in many cases have defining character, and determine our understanding of what vulnerability is, rather than providing a scale to measure it. Typically, hierarchical composite indicator frameworks are used, which combine qualitative and quantitative aspects [START_REF] Birkmann | Risk and vulnerability indicators at different scales: Applicability, usefulness and policy implications[END_REF][START_REF] Cutter | GI Science, Disasters, and Emergency Management[END_REF], for instance breaking down the vulnerability of a region against a hazard into the dimensions 1. Geography and environmental conditions; 2. Socio-Economic capacity, infrastructure systems; 3. Response capacity. Procedures for indicator selection balance theoretical views on vulnerability with pragmatic considerations of data collection and availability of information [START_REF] Birkmann | Risk and vulnerability indicators at different scales: Applicability, usefulness and policy implications[END_REF]. In Table 1, we provide an overview indicators relevant for Typhoon Haiyan. The table is based on a survey of papers focusing on vulnerability of coastal regions or islands against storms and floods. From an initial set of more than 47 papers that we found via a google scholar search, we than extracted those that provided an indicator system: [START_REF] Adger | Vulnerability[END_REF][START_REF] Balica | Flood vulnerability indices at varying spatial scales[END_REF][START_REF] Birkmann | Measuring Vulnerability To Natural Hazards[END_REF]Brooks, Neil Adger, & Mick Kelly, 2005;[START_REF] Fekete | Validation of a social vulnerability index in context to river-floods in Germany[END_REF][START_REF] Turvey | Vulnerability Assessment of Developing Countries: The Case of Small-island Developing States[END_REF]. Per indicator we provide also the functional relation to vulnerability (increasing (+) or decreasing (-)) and its category: exposure (Exp), fragility (Frag) or resilience (Res).

Interestingly, most authors focus on the socio-economic capacity: indicators characterizing the vulnerability of the population, and its proximity to the area at risk are among the most frequent indicators. For the geographical and environmental conditions, the coastline is the only indicator used by several authors. This may be related to the fact that the indicators in this category relate to the exposure, which is not always understood as component of vulnerability. The indicators for response capacity are unique for each paper, indicating the fragmented understanding of how the ability to manage and respond to a disaster can be predicted. Interestingly, international interventions and the ability to coordinate with the UN system or international volunteer communities, for instance, are completely missing, highlighting that preparedness is still understood as a local or national endeavour.

Long Paper -Decision Support Systems Proceedings of the ISCRAM 2015 Conference -Kristiansand, May 24-27

Palen, Büscher, Comes & Hughes, eds. 1 also shows that typically, indicators provide a static snapshot of the situation. Mostly, indicators are based on statistical data on a national or broader regional scale, or indices (such as Government effectiveness). Trends and patterns, such as steep decline of the health care system, or deterioration of living conditions cannot be detected with such systems.

Due to the variety of indicators, measured on different scales, a normalization process turning the data into comparable information is required. Most commonly, an indicator s i is be normalised using a fragility function f i d indicating to which extent value v(s i ) of indicator i will increase or reduce the vulnerability in one of the above dimensions d. Typically, [ ] , where 1 represents total vulnerability, whereas 0 indicates that there is no contribution to the vulnerability within this dimension. Resilience and exposure functions are designed similarly (Merz et al., 2012). To model how indicators for different dimensions contribute to the overall vulnerability, they are aggregated first intra-, then inter-dimensional. The most common approach is using a linear aggregation function. [START_REF] Nanduri | Aggregating physical intensity indicators: results of applying the composite indicator approach to the Canadian industrial sector[END_REF] provide an overview of alternative models.

Early-Warning Indicator Systems

In general, the indicators of typhoon EWS are only used during the monitoring phase before the disaster. Warnings in this context are based on meteorological forecasting models. Often, meteorologists refer to these as the "spaghetti models" because when laid out on a map, the storm paths resemble strings of spaghetti. To monitor a typhoon, the EWS usually take into account some key indicators, identified by [START_REF] Gray | Tropical Cyclone Genesis[END_REF], such as sea surface temperatures above 26°C; moist tropical atmosphere; broad-scale convection; low-level inflow with upper outflow and weak vertical wind shear.

After a desk review and literature research (NOAH Project, 2014;[START_REF] Ng | The Philippines government develops disaster early warning system[END_REF][START_REF] Neussner | Manual -Local early warning systems -Experiences from the Philippines[END_REF][START_REF] Briones | Case Study : Typhoon Haiyan in the Philippines[END_REF][START_REF] Neussner | Manual -Assessment of Early Warning Efforts in Leyte for Typhoon Haiyan/Yolanda[END_REF], we selected in Table 2 a list of indicators that were used in the response to Typhoon Haiyan. This list reflects our understanding, resulting from academic literature, project reports, evaluations, satellite imagery (PAGASA cyclone map, meteo Doppler 1 for precipitations), and news reports. Other signals, such as changing tides, anxious animals, rumours, and word of mouth, rely on sensual and first hand impressions, that were not accessible to us. We are aware that the list is not complete in this respect. Some satellite pictures were useful ut also some indicators such as the ones presented in the next table. 1 A weather radar is a type of radar used in meteorology to locate precipitation, calculate their movement and determine their type (rain, snow, hail, etc.). The three-dimensional structure of the data obtained allows inferring the movements of clouds and precipitation in and identifying those that may cause damage. Finally, using rainfall as tracers, we can deduce the radial direction and speed of winds in the lower atmosphere. 1 m

Comparing Tables 1 and2, there is no overlap between the indicators, although the problems are interrelated: EWSs should provide timely estimates of potential risks to protect the most vulnerable people and to prepare for replacing the most vulnerable infrastructures (cf. Figure 1). Moreover, the vulnerability indicators hide the time dimension; important trends or relations between the indicators are neglected, whereas most EWS indicators capture a development in a given amount of time.

Some steps towards integrating real-time forecasting into EWS have ben made. For instance, the Hurricane component of FEMA's HAZUS Model provides prediction models based on wind-induced loads, building response, damage, and then loss, rather than simply using historical loss data to model loss as a function of wind speed, [START_REF] Yau | Hurricane Damage and Loss Estimation Using an Integrated Vulnerability Model[END_REF]. Such a model, however, require complete and reliable data sets on building infrastructure, economy, and More importantly even, there is no clear indication which actions the population, or a humanitarian organization should take, and there is no structure component to reconcile the conflicting preferences and orchestrate the response.

THE RESPONSE TO TYPHOON HAIYAN

The Philippines are confronted with some of the highest disaster risks worldwide: with respect to earthquakes, storms, and flooding it is ranked among the 10 countries with highest mortality [START_REF] Mosquera-Machado | A comparison of selected global disaster risk assessment results[END_REF]. Therefore, levels of preparedness are relatively high [START_REF] Brower | Evolving and Implementing a New Disaster Management Paradigm: The Case of the Philippines[END_REF]. The Philippines Disaster Risk Reduction and Management Act of 2010 2 shapes the preparedness and response operations in the Philippines, assigning the role of coordination to the National Disaster Risk Reduction and Management Council (NDRRMC).

Early Warnings

Table 3 summarizes relevant EWS indicators, the categories and respective thresholds used for Typhoon Haiyan (CEDIM, 2013;Weather Unisys, 2013;NOAA, 2013;AGORA, 2013a,b,c). Haiyan was first noticed as a weather disturbance with the potential of developing into a tropical storm on 3 November 2013 [START_REF] Neussner | Manual -Assessment of Early Warning Efforts in Leyte for Typhoon Haiyan/Yolanda[END_REF], Indeed, by exceeding all relevant thresholds, EWS in South Eastern Asia, predicted that Haiyan would severely harm the provinces of Leyte and Eastern Samar. Early warnings were issued two days before Haiyan's landfall by PAGASA (Philippines Atmospheric Geophysical and Astronomical Services Administration) and JTWC (Joint Typhoon Warning Centre). On November 7 th , 2013, the Japan Meteorological Agency top windspeeds of 315 km/h [START_REF] Neussner | Manual -Assessment of Early Warning Efforts in Leyte for Typhoon Haiyan/Yolanda[END_REF]. Few hours later, Haiyan made its first landfall in the Philippines at Guiuan, Eastern Samar, without losing intensity. Haiyan reached level 3 of storm surge (5.2 m in Tacloban Airport (PAGASA, 2014)), level 3 for flood warning, level 5 for wind speed (230 km/h and 315 km/h, (PAGASA, 2014)), the level "Red" for heavy rain warning and all 4 levels of Public Storm Warning Signal were triggered by region (NASA, 2014)).

Despite these warnings, initial reports estimated 4.3 million people to be affected by Typhoon Haiyan. Later, the number rose later over 14 million people, of which more than 4 million lost their homes and livelihoods 3 .

From Early Warning to Response -A role for Vulnerability Assessment and Decision Analysis

Although the impact could not be precisely predicted, massive damage was expected. In the transition phase from warning to response, the Department of Social Welfare and Development (DSWD) engaged with local municipalities to evacuate families in particularly exposed areas, identified adequate evacuation centers or reinforced the roofs of buildings 4 . The military was activated, volunteers were deployed; transportation systems and trucks pre-positioned and food packages and medical aid kits were packed (NDRRMC Sit Rep No. 4). The ASEAN states sent an AHA team to Manila to prepare for the response, and several NGOs that were already active in the country such as ICRC prepared to respond onsite. Dispatching and deploying decisions need to be made quickly, using forecasts and preliminary assessments as well as professional experience and context-based knowledge. Decisions in this phase are therefore are necessarily based on uncertain and incomplete information that cannot be verified until after the disaster. National authorities appeared to be highly aware of needs and had accurate baseline information. Many interviewees during the field visit of the DRL team [START_REF] Chan | Innovative Research Design -A Journey into the Information Typhoon[END_REF]Van de Walle & Comes, 2014), described these strengths, which were considered as "of extraordinary quality as compared to other disasters". However, Common Operational Datasets (CODs) needed to be improved and completed during the response.

Because of the high pressure, this happened often in parallel in government agencies and by NGOs (Ebener, 3 The fact that most pre-deployed staff and goods were routed to the hubs of Manila and Cebu instead of to the most vulnerable areas such as the rural areas of Leyte and Eastern Samar, implied that goods arrived particularly late in the hardest hit regions. Additionally, efforts to reinforce buildings came too late to have a considerable impact, highlighting the importance of taking into account the temporal aspects and intervention windows. Therefore, we propose a decision analytical approach that takes into account EWS and vulnerability information to initiate the response.

DISCUSSION, CONCLUSION AND OUTLOOK

Typhoon Haiyan highlighted the lack of effectiveness of current EWS: it had been clearly understood that the damages would be massive some days before the Typhoon made landfall 5 . However, the problems with communications of the storm surge and the lack of shelters prevented many people from seeking, or if they sought, in finding safe shelters -resulting in the death of hundreds [START_REF] Heydarian | Little preparation for a great disasters[END_REF]. The vulnerability of population and critical infrastructures such as healthcare, communication, or food and water supply has not been integrated into, making it difficult to predict where help will be needed most urgently. To this date, the early response decisions are largely based on experience instead of structured decision support (Van de Walle & Comes, 2014)..

EWS are designed to initiate the response. Yet, simple warnings about the nature of an event are not sufficient. Rather, the warning should reflect the context and possible mitigation measures that the decision-makers can choose, such as reinforcing shelters and setting up emergency telecommunication systems; pre-positioning fleet of trucks, food and water supplies; evacuation; and activation of the local or international response. Since these strategies determine when a warning should be issued, and which information it needs to convey, EWS can no longer be understood as a binary problems, where the decision consists in issuing an alert (or not). Figure 3 shows the situation as it is today. On the basis of continuous vulnerability functions, categories are formed that are used to prioritise disaster risk reduction efforts and allocate resources, focusing mostly on longer-term development aims. Contrarily, EWS thresholds are often defined in terms of a simple binary scale, reflecting tipping points above which an intervention (of unclear nature) is necessary. There is currently no systematic or explicit integration between both.

To ensure that warnings respect the preparation times (from an alert to completed preventive action), we propose to elaborate a decision model that uses vulnerability and EWS indicators as well as potential interventions to determine appropriate thresholds that take into account the local context. The concept is shown in Figure 4, which outlines that the different vulnerability levels should be aligned with the EWS thresholds. Warnings are not issued any more on basis of a simple step function; rather we envision various levels with intrinsic continuous escalations (see Figure 4). In this way, preferential information about the most critical aspects for the society can be combined with the information about expected impact and damage of a hazard.

Figure 3 :

 3 Figure 3: Isolated Vulnerability Assessments (left) and EWS (right)

  Long Paper -Decision Support Systems Proceedings of the ISCRAM 2015 Conference -Kristiansand, May 24-27Palen, Büscher, Comes & Hughes, eds.

Figure 4 :

 4 Figure 4: Our Vision: Interplay of Vulnerability (left) and EWS (right)For both systems, we would like to emphasize the importance to define warnings as an interpretation of the values given by an aggregation function combining both indicators of EWS and vulnerability. While Figures3 and 4only show individual indicators, the overall vulnerability or warning decision should take into account the interplay of indicators. For instance, one can elaborate an overall score of vulnerability; derive thresholds; and then combine these with respective overall scores of EWS indicators. The simple aggregation function usually used is the weighted sum where weights associated to indicators can be determined according to the preferences of government authorities and experts in EWS and vulnerabilities. In this way, preferential information about the most critical aspects for the society can be combined with the information about expected impact and damage of a hazard.

  reliable, accurate and specific: interpretation is stable and consistent over time  Sensitive: reflects changes in what is measured or synthesized

 Understandable, simple and usable by all stakeholders  Relevant relative to the objective  Acceptable cost compared to the service rendered  Useful: adding information to the decision-making

Table 1 : Selection of Typhoon Relevant Vulnerability Indicators

 1

	Indicators	f	Unit	Vul	al.,, 2009	Balica, et	2007	Turvey,	Adger, 2006	2009	Fekete,	Adger, 2005	Brooks &	2006	Birkmann,
	Geography and Environmental Conditions
	Sea-level rise	Exp mm/year +		x
	Storm surge	Exp Cm	+		x
	# of cyclones	Exp #	+		x
	River discharge	Exp m 3 /s	+		x
	Foreshore slope	Exp %	-		x
	Soil subsidence	Exp m 2	+		x
	Coastline	Exp Km	+		x		x	x
		Socio-Economic Capacity
	Cultural heritage	Exp #	+		x
	Population Density	Frag %	+									x
	Population close to coastline	Exp # People +		x		x	x		x
	Growing coastal population	Exp %	+		x		x
	Urban growth	Exp %	+				x
	Arable land	Frag %	+					x
	Percentage Female	Frag %	+							x				x
	Percentage people under 10/12/18	Frag %	+							x				x
	Percentage of people over 60/65	Frag %	+							x				x
	% of disabled persons	Frag %	+		x
	Nutrition	Frag kcal/p	-				x
		Response Capacity
	Shelters	Res #	-		x
			# events/
	Awareness	Res	10 years -		x									x
	Presence EWS	Res Binary	+											x
	Implementation of buffer zones	Res Binary	+											x
	Estimated recovery time	Res days	+		x
	Drainage	Res km	-		x
	Number of physicians	Res #/1000 p -				x
	Number of hospital beds	Res #	-				x
	Control of corruption	Res Index	-									x
	Government effectiveness	Res Index	-									x
	R&D investment	Res % GNP -									x
	Social networks	Res %	+											x

Table 2 : Typhoon Early Warning Indicators

 2

	Indicator	Description

Table 3 : Early Warning Indicators and Thresholds for Haiyan

 3

	Warnings	Indicators	Levels	Thresholds
			1	. < 2m
	Storm surge warnings	Storm surge	2	2m < . < 5m
			3	. > 5m
			1	< 129mm
	Flood warnings	24-hours rainfall	2	129mm < . < 240mm
			3	. > 240mm
	Public Storm Warning	Wind speed	1	30-60 km/h within 36 hours

2 See http://www.ifrc.org/docs/idrl/878EN.pdf Palen, Büscher,

Comes & Hughes, eds.

  The Digital Humanitarian Network (DHN) was activated by UN-OCHA on November 7, 2013. Members of the DHN network and the larger Virtual and Technology community mobilized. MapAction, Humanitarian Open Street Map, GIS Corps, ESRI Disaster response Program, Translators and Statistics without Borders, Info4Disaster and many others activated their networks, working remotely or sending volunteers into the disaster struck area.

  UN OCHA situation report number 34, as of January 28 2014 http://reliefweb.int/report/philippines/philippines-typhoon-haiyan-situation-

	Signals (PAGASA, Castro, & Dimailig, 2014).	expected within	2	60-100 km/h within 24 hours
	2014)	period of time	3	100-185 km/h within 18 hours
			4	Greater than 185 km/h within 12 hours
			1	33-42m/s, 119-153 km/h
	Saffir-Simpson hurricane wind scale	Top wind speed	2 3 4	43-49 m/s, 154-177 km/h 50-58 m/s, 178-208 km/h 58-70 m/s, 209-251 km/h
			5	>70 m/s, > 252 km/h
		Observed	1 -Yellow	7.5-15 mm within 1 hour ; most likely to continue for next 3 hours.
	Heavy Rain warning (PAGASA, 2014; Palafox, 2014)	rainfall and rainfall amount from Doppler radars	2 -Orange 3 -Red	15 -30 mm within 1 hour ; most likely to continue; or continuous rainfall for past 3 hours is more than 45 mm to 65 mm. More than 30mm within 1 hour or continuous rainfall for past 3 hours with more than 65mm.
	report-no-34-28-january-2014
	4 http://www.gov.ph/2013/11/06/dswd-preps-for-possible-impact-of-storm/
		Long Paper -Decision Support Systems
	Proceedings of the ISCRAM 2015 Conference -Kristiansand, May 24-27
		Palen, Büscher, Comes & Hughes, eds.