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Numerical modeling to determine risk scenarios in an alpine alluvial fan

P. Aleotti1, G. Polloni1, D. Tropeano2 & L. Turconi2 1Eng. Geology and Geotechnical Dept., Aquater S.p.A., S. Giuliano, Italy 2CNR-IRPI – Research Institute for Hydrological and Geological Hazard Prevention, Torino, Italy

Abstract

In this paper a methodological approach for hazard and risk zonization in an alpine alluvial fan is described. The method is based on the application of the FLO-2D software, a two-dimensional hydraulic model that predicts the areas prone to inundation and estimates the probable range of flow velocity and depth. In the computation, a 200 year return period hydrograph was used and different scenarios were analyzed starting from the actual situation and simulating the occurrence of collateral phenomena such as the partial occlusion of the bridge located at the fan apex or the rupture of the dikes embanked along the riversides in the fan area. The presence of hypothetical defensive levees of different height and length was also simulated. In order to define the event intensity, the adopted approach uses a combination of flow depth and velocity similar to those proposed by other authors for the Swiss and Austrian Alps. Finally, a numerical index is introduced allowing the comparison of the scenarios and, therefore, the determination of the most suitable protective measures.

1 Introduction

Along alpine valley floors, fans, built up by lateral tributaries, are favored sites for settlements and human activities because they are not as steep as the valley flanks and not as marshy as the frequently flooded valley floors. Fans however, are very hazardous areas because they are often affected by severe flooding and mass transport phenomena that cause serious damage and injuries in the urban centers located therein. The studied area (Isorno alluvial fan) is located in the Italian Western Alps at the exit of a lateral tributary (Isorno torrent) in the valley bottom of the Toce River (Ossola Valley), fig. 1.

© 2004 WIT Press, www.witpress.com, ISBN 1-85312-736-1Risk Analysis IV, C. A. Brebbia (Editor)

Stratigraphical and morphological evidences indicate that the Isorno alluvial fan is a mixed fan, i.e. it is the result of the alternation of fluvial and debris flow processes. Starting from the historical reconstruction of past events, the adopted methodology allows the evaluation of the actual hazard on the alluvial fan area and then the definition of various hypothetical risk scenarios.

Figure 1: Location of the studied area (Italy, Piedmont Region colored in black

in the picture in the upper right corner). White line is the Isorno alluvial fan (Isorno T.), arrows indicate the analyzed scenarios.

2 Geological setting

The general geological setting of the studied area is very complex because many of the tectonic units that built the Italian Western Alps outcrop in a relatively narrow belt (Pennidic Domain). Rocks are mainly micaschists, paragneiss and anphibolites, while sediments in the alluvial fan area include both sediments of fluvial origin and debris flow deposits. The first ones are generally moderately sorted, weakly imbricated cobbles and gravels, but also laminated medium to fine sands, while debris flow deposits mainly consist of weakly stratified matrix supported angular clasts.

3 Adopted methodology

The adopted methodology is based on the use of the FLO-2D software [8], a two dimensional flood routing model that routes a flood hydrograph using the full

TOCE R.

Bridge at the fan apex

ISORNO T.

12


154 Risk Analysis IV

dynamic wave momentum equations and guarantees volume conservation to accurately predict the area of inundation. The fluid viscosity and yield stress terms are accounted in the model. This model, designed for non-Newtonian flood flows on alluvial fans, can also predict the probable range of flow velocity (v) and depth (d). The intensity (or severity) of a given event on the alluvial fan was calculated as a function of the flow depth and velocity, as shown in table 1. In the calculation of risk, it was assumed that the degree of risk for a given element (building) is coincident with the relevant level of hazard (event intensity). Such an assumption is simplifying because it is equivalent to disregarding the vulnerability.

Table 1: Event intensity.

Event intensity First condition Second condition Very low d < 0.25 and v < 0.25 Low 0.25 ≤ d < 0.5 and v < 0.5 or d ≤ 0.25 and 0.25 ≤ v < 0.5 Medium 0.5 ≤ d < 1.0 and v < 1.0 or d ≤ 0.5 and 0.5 ≤ v < 1 High d ≥ 1.0 or v ≥ 1.0

4 Scenarios, input and general assumptions

Two different main scenarios were analyzed: the first one takes into account the partial occlusion of the bridge span at the fan apex while the second simulates the rupture of the dikes located on the left bank of the torrent, fig. 1. A digital terrain model was obtained from aerial photos and square cells (5x5 m) were adopted in the computations. The hydrograph (200 year return time) was calculated by means of the FLEA software [16]. The clear water hydrograph was used as input in the model following empirical bulking of the volume of water by imposing a reasonable sediment concentration. It was assumed that the sediment concentration, measured as the ratio of sediment to water by volume, varied uniformly with discharge (i.e. non lag time between peak discharge and peak concentration) from a minimum of 0.05 to a maximum of 0.20. The total sediment volume calculated during the simulation is consistent with the one estimated by Pech [14] for the 1978 flooding event which is the most recent episode that affected the alluvial fan. Rheologic parameters, yield stress and viscosity, were selected on the basis of literature data in similar situations [12].

5 Results

In the first scenario the flow involved a wide area of the alluvial fan, but with velocities usually under 0.5 m/s and depths lower than 0.3 m. For this reason the calculated event intensity is generally low or very low. The second scenario is heavier because both the velocities and the flow depths are generally higher than those calculated in the first simulation (v > 1.5 m/s; d > 1 m), fig. 2. As a consequence the event intensity is generally


Risk Analysis IV 155

higher and almost 10% of the whole fan area falls in the high intensity class. For this reason, starting from this situation, further analyses were carried out by simulating the presence of complementary levees of different height and length (Table 2). In scenario 2a the flow separates into two distinct branches: both branches go around the levee but only the lower one overflows it at the peak discharge. Such an overflowing doesn’t occur by further elevating the levee (scenario 2b), and this probably explains why velocities and depths computed in the lower branch are all higher than those calculated for scenario 2a. By further extending the levee (scenario 2c), the flow diversion is obviously largest, but velocities are considerably lower than those recorded for the other scenarios. Every scenario corresponds to a different areal distribution of the intensity and, also, to a different frequency of the various classes in which intensity was divided (very low, low, medium, high), fig. 3.

Figure 2: Results of the simulations.

Generally it appears that the presence of the levee induces a widespread decrease both of the extension of the affected area and of event intensity: this effect is particularly evident downstream of the structure where the event intensity changes from medium-high to low-very low, fig.2. Nevertheless in some sectors of the fan the event intensity is still high, as along the belts involved by the flow diversions (upper and lower branches in scenarios 2a and 2c, only upper branch in scenario 2b). Therefore it is interesting to observe that in scenario 2b the assessed intensities in the upper branch are averagely higher due to higher velocities. More precisely, although on one hand the most significant lowering of the total flooded area is related to scenario 2b, on the



other, the largest frequency of the “high intensity” class is associated to this hypothesis. Instead scenarios 2a and 2c are almost equivalent to each other: in fact the reduction of the affected area (scenario 2c) corresponds only to a reduction of the “medium intensity” class while the frequency of the “high intensity” class is approximately the same.

Table 2: Characteristics of the scenarios.

Scenario Levee Presence Height (m) Length (m)

2 No - - 2a Yes 2 420 2b Yes 10 420 2c Yes 2 500

Figure 3: Results of the simulations.

6 Discussion

The construction of the levee doesn’t induce a significant reduction in the total number of buildings involved by the flow but, rather, a lowering in the frequency of the medium-high risk classes compensated by a corresponding increase of very low-low risk classes. This trend is more evident in scenarios 2a and 2c and less evident in scenario 2b. Nevertheless in scenarios 2a and 2b the presence of the “short” levee locally causes an important increase in the risk along the upper branch if compared to the situation without the structure, fig. 4. By extending such a structure (scenario 2c) this paradoxical effect is less manifest. The results and particularly the above-mentioned considerations about the contrasting effect that the construction of protective measures may have on the spatial distribution of hazard and risk, highlight that frequently the definition of the optimal defense solution can be a very difficult task. Nevertheless a quantitative assessment can be carried out by using a matrix in which the actual risk (risk without levee) and

0.E+00

1.E+05

2.E+05

3.E+05

4.E+05

5.E+05

very low low medium highevent intensity

flooded area (sqm)

scenario 2 scenario 2a scenario 2b scenario 2c

0

10

20

30

40

50

very low low medium high totalrisk for buildings

number of buildings



the modified risk (residual risk after the construction of the levee) are compared (Table 3).

Figure 4: Risk maps in various scenarios. Dotted line represents the path of the

flow in the actual situation (without levee, scenario 2). Thick vertical line is the levee (2a, 2b), dotted vertical line is the prolongation of the levee (scenario 2c). Circles show buildings in which an increase of risk as a consequence of the construction of the levee is observed.

Table 3: Matrix with relevant weights (w).

Actual risk Residual risk Nil Very low Low Medium High High +4 +3 +2 +1 0 Medium +3 +2 +1 0 -1 Low +2 +1 0 -1 -2 Very low +1 0 -1 -2 -3 Nil 0 -1 -2 -3 -4

The matrix has a neutral line corresponding to the diagonal of the square (the structure doesn’t induce a modification of the risk level in a given building) that divides the field into an upper part (the structure induces an increase of the risk level in a given building) and a lower part (the structure induces a decrease of the risk level in a given building). By analyzing the redistribution of the elements (buildings) in the different classes of the matrix (cells), it is possible to have a first qualitative evaluation of



the effect induced by a certain project, fig. 5. For a quantitative evaluation a weight was assigned to each cell of the matrix proportional to the degree of variation, positive or negative, that a certain cell implies. For example, the cell corresponding to the combination “high risk – risk nil” (which indicates that for a given element the actual risk is high and the modified, residual risk is nil) has a weight w = -4 (the project induces on a specific element a strong reduction of the risk) and, on the opposite extreme, the cell “nil risk – high risk” has a weight w = +4 (the project causes a strong increase of the risk). All the other cells define the intermediate circumstances following the symmetry axis of the neutral line where w = 0. Finally a total risk index (IR) for a given project is calculated as (1):

(1)

where Ri = percentage of elements that fall in the class i, wi = weight of class i in the matrix, n = total number of classes in the matrix. The lowest value of IR, namely the maximum decrease of the risk, was calculated for scenario 2c (IR = -7.2), while the risk index was IR = -6.4 and IR = -5.3, for scenario 2a and 2b, respectively. With respect to this result it is interesting to observe that, length of the levee being equal (scenarios 2a and 2b), the structure that ensures the largest reduction of risk is the lower one.

Figure 5: Values (expressed as percentage on the total) of the various cells for

the different scenarios (N: nil, VL: very low, L: low, M: medium, H: high).

7 Conclusions and perspectives

Hazard and risk in Italian Alpine alluvial fans are frequently very high, as still further demonstrated during several events that affected these areas recently [1, 4, 18]. In the Piedmont Region alone, where the study area is located, more than 2000 active alluvial fans have been inventoried. This explains why alluvial

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DECREASE INRISK

NEUTRALLINE

INCREASE INRISK

ACTUAL RISKN VL L M H

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R+3

R+3

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R+1

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R-1

R+1

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R-1

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20 1.8 10.9 5.4

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3.63.61.8

18.2 7.3 5.5

5.51.8 3.6

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1.8 1.8

5.5 12.7 3.6

3.63.6



fans have become a topic of major interest for both geoscientists and national and local administrations. The methodological approach described in this article arises from the necessity to set up a useful tool for an objective and sufficiently exhaustive hazard analysis. Similar techniques were proposed by other authors and applied to other fans in various environmental contexts [2, 3, 7, 9, 10, 11, 15]. To start a simulation, a suitable topographic map with sufficient detail of potential flow surface and a flood hydrograph are necessary. The detail of the topographic map should be compatible with the level of detail required in a given project. In general the modeling detail should be consistent with the accuracy and resolution of the flood hydrology and mapping, and also with the extension of the studied area. Very large flood events require less detail than shallow flooding. In addition the accuracy of the predicting water surface elevations cannot exceed the accuracy of the mapping and survey data [8]. Nevertheless, the most important and the most uncertain aspect of simulations performed with hydraulic models is the estimation of the flow hydrograph. This is all the more true for debris flows where sediment concentration must also be considered. As the potential flood hazard is defined as a discrete combined function of the event intensity and of the occurrence probability, in this work a 200 year return period hydrograph was used as input in the modeling so that the results in terms of both inundated areas and depth and velocity of the flow assume a proper probabilistic meaning. Regarding the risk analysis some simplifications were adopted as building vulnerability was not taken into account: this implies that in the results the degree of risk for a given element is coincident with the its level of hazard (event intensity). To take into account the vulnerability of the constructions, it is necessary to conduct a detailed survey of the characteristics of the buildings and define a mechanical model to evaluate the collapse resistance of structural and non-structural elements impacted by fluids in movement [6]. Both the above mentioned topics will represent the further evolution of this work On the other hand the proposed methodology allows, starting from the availability of a digital terrain model and a hydrograph, a rapid hazard assessment that can then be validated on the basis of field works and historic studies on past events [2, 5, 17]. Moreover the adoption of the matrix and of the numerical index allows the quantitative comparison of different risk scenarios and, hence, the selection of suitable protective measures.

References

[1] Aleotti, P. & Polloni, G., Risk scenario in mountain alluvial fans: some examples in Italian Central Alps. Proc of the 8th Congress of the International Association of Engineering Geology, Vancouver, Canada, 21-25 September 1998, 3, pp. 2051-2058, 1998.

[2] Aleotti, P., Canuti, P., Falorni G., Fanti, G., Grimaldi, G., Guida, D., Lombardi, G. & Pappalardo, G., Assessment of potential debris flow



inundation areas on a small alluvial fan in Southern Italy. Proc. of the Int. Conference on Fast Slope Movements: prediction and prevention for risk mitigation, Naples, May 11-13, 2003, pp. 9-15, 2003.

[3] Aleotti, P. & Polloni, G., Two-dimensional model of the 1998 Sarno debris flows (Italy). Proc. of the 3rd International Conference on Debris Flow Hazard Mitigation: Mechanics, Prediction and Assessment, Davos, September 10-12, 2003, 1, pp. 553-563, 2003.

[4] Anselmo, V., Guiot, E., Ceriani, E., Risk analysis of developed areas on alluvial fans. Proc 10th International Symposium Interpraevent 2004, Riva del Garda - Italy, 24-28 may 2004, 2004.

[5] Coe, J.A., Godt, J.W., Parise, M. & Moscariello, A., Estimating debris flow probability using fan stratigraphy, historic records and drainage-basin morphology, Interstate 70 highway corridor, central Colorado, USA. Proc. of the 3rd International Conference on Debris Flow Hazard Mitigation: Mechanics, Prediction and Assessment, Davos, September 10-12, 2003, 2, pp. 1085-1096, 2003.

[6] Faella, C., & Nigro, E., Dynamic impact of the debris flows on the constructions during the hydrogeological disaster in Campania 1998: failure mechanical model and evaluation of impact velocity. Proc. of the International Conference on Fast Slope Movements: prediction and prevention for risk mitigation, Naples, May 11-13, 2003, pp. 179-186, 2003.

[7] Fiebieger, G., Hazard mapping in Austria. Journal of Torrent, Avalanche, landslide and Rockfall Engineering, 134 (61), 1997.

[8] FLO-2D, Users’ manual. Version 2003.06, Tetra Tech FLO2D Software Inc., 2003.

[9] Garcia, R., Lopez, J.L., Noya, M., Bello, M.E., Bello, M.T., Gonzales, N., Paredes, G., Vivas, M.I., & O’Brien, J., Hazard mapping for debris flow events in the alluvial fans of Northern Venezuela. Proc. of the 3rd International Conference on Debris Flow Hazard Mitigation: Mechanics, Prediction and Assessment, Davos, September 10-12, 2003, 1, pp. 589-599, 2003.

[10] Gostner, W., Bezzola, G.R., Schatzmann, M. & Minor, H.E., Integral analysis of debris flow in Alpine torrent – the case study of Tschengls. Proc. of the 3rd International Conference on Debris Flow Hazard Mitigation: Mechanics, Prediction and Assessment, Davos, September 10-12, 2003, 2, pp. 1129-1140, 2003.

[11] Nakagawa, H., Takahashi, T., Estimation of a debris flow hydrograph and hazard area. Proc. the 1st International Conference on Debris Flow Hazard Mitigation: Mechanics, Prediction and Assessment, San Francisco, California, August 7-9, 1997, pp. 64-73, 1997.

[12] O’Brien, J.S., Julien, P.Y. & Fullerton, W.T., Two-dimensional water flood and mudflow simulation. Journal of Hydraulic Engineering, ASCE, 119 (2), pp. 244-259, 1993.

[13] OFEE, OFAT, ODEFP (eds), Prise en compte des dangers dus aux crues dans le cadre des activités de l’aménagement du territoire. Bienne, 1997.



[14] Pech, P., Les crues du 7 aout 1978 dan l’Ossola. Revue de Geographie Alpine, 78, pp. 89-101, 1990.

[15] Petrascheck, A. & Kienoltz, H., Hazard assessment and mapping of mountain risk in Switzerland. Proc. of the 3rd International Conference on Debris Flow Hazard Mitigation: Mechanics, Prediction and Assessment, Davos, September 10-12, 2003, 1, pp. 25-38, 2003.

[16] Ranzi, R. & Rosso, R., FLEA – Flood Event Analyzer. Users manual, version 3.01, 1997.

[17] Tropeano, D., Turconi, L. Using historical documents in landslide, debris flow and stream flood prevention. Application in Northern Italy. Natural Hazard, Kluwer Academic Publishers, The Netherlands, in press.

[18] Tropeano, D., Turconi, L., Rosso, M., Cavallo, C., The October 15, 2000 debris flow in the Bioley torrent, Fenis, Aosta Valley, Italy – damages and processes. Proc. of the 3rd International Conference on Debris Flow Hazard Mitigation: Mechanics, Prediction and Assessment, Davos, September 10-12, 2003, 2: 1037-1048, 2003.



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