Debris flow modeling for susceptibility mapping at regional to national ...

Landslides and Engineered Slopes: Protecting Society through Improved Understanding – Eberhardt et al. (eds) © 2012 Taylor & Francis Group, London, ISBN 978-0-415-62123-6

Debris flow modeling for susceptibility mapping at regional to national scale in Norway L. Fischer, L. Rubensdotter, K. Sletten & K. Stalsberg Geological Survey of Norway (NGU), Trondheim, Norway

C. Melchiorre Department of Earth Sciences, Uppsala University, Sweden

P. Horton & M. Jaboyedoff Institute of Geomatics and Risk analysis (IGAR), University of Lausanne, Switzerland

ABSTRACT: Debris flows and related landslide processes occur in many regions all over Norway and pose a significant hazard to inhabited areas. Within the framework of the development of a national debris flows susceptibility map, we are working on a modeling approach suitable for Norway with a nationwide coverage. The discrimination of source areas is based on an index approach, which includes topographic parameters and hydrological settings. For the runout modeling, we use the Flow-R model (IGAR, University of Lausanne), which is based on combined probabilistic and energetic algorithms for the assessment of the spreading of the flow and maximum runout distances. First results for different test areas have shown that runout distances can be modeled reliably. For the selection of source areas, however, additional factors have to be considered, such as the lithological and quaternary geological setting, in order to accommodate the strong variation in debris flow activity in the different geological, geomorphological and climate regions of Norway. 1

INTRODUCTION

Debris flows and related landslide processes occur in many regions all over Norway and pose a significant hazard to inhabited areas and transportation corridors (Jaedicke et al., 2008, 2009). Within the framework of the production of a national debris flows susceptibility map, we are working on a modeling approach that is suitable for the identification of potential debris flow areas with a nationwide coverage. Debris flows and related landslide processes can occur in various terrain types, provided that enough soil or debris material is available and the slope angle is steep enough. Confined and unconfined debris flows can be distinguished by the characteristics of the source and deposition area (Lorente et al., 2003). Zimmermann et al. (1997) proposed four different source areas types, two of them being released from open-slope terrain and two in channelized topography. Confined debris flows develop within surface depressions and channels that may contain an ephemeral or perennial creek. Unconfined debris flows occur from openslope terrain on hillslopes not previously incised,

typically with abundant unconsolidated sediments, steep gradients and scarce plant cover (Brunsden 1979). Both unconfined and confined debris flows occur in Norway and must be considered in this susceptibility map. Debris flow modeling at medium scale has been subject of various studies in the last decades (e.g., Zimmermann et al., 1997, Horton et al., 2008, Blahut et al., 2010). For a large-area susceptibility assessment, all areas possibly affected by the debris flows have to be identified. The occurrence of debris flows is controlled by the long-term, more or less constant factors such as relief, geological setting, substratum type, availability of debris, but is also strongly influenced by variable factors such as precipitation, heavy snowmelt events, or lake outbursts. However, these variable factors cannot be considered for a country-scale susceptibility analysis, so the focus is on the constant factors. In this study, we use GIS-based approaches incorporating an index-based detection of the source areas and a simple assessment of the debris flow runout to develop a substantial basis for a debris flow susceptibility assessment all over Norway.

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2

STUDY AREAS

Norway comprises the western part of Scandinavia with an area of 323,800 km2 over 1800 km from latitude 58°N to 71°N. Approximately 30% of the total land area is occupied by mountains, with highest peaks up to 2469 m above sea level (asl) and steep slopes over 30° cover 6.7% of the country (Jaedicke et al., 2009), corresponding to almost 21,700 km2. The geology is mostly granitic and gneissic rocks, but slate, sandstone, limestone and marine deposits are also common. Because of the large latitudinal range of the country and the varied topography, the country has different climatic areas. Along the coast, climate is influenced by the North Atlantic Current, whereas the inland experiences a more continental climate. To accommodate the large variation in topography, geomorphology and climate over Norway, we have chosen different sites for the method testing and model calibration (Fig. 1). Study area 1 is located in the coastal area of Troms county in northern Norway. The area has a humid climate with up to 3000 mm precipitation per year and mean annual temperatures between −1 and 4°C, mainly depending on the respective altitude (seNorge.no 2011). The bedrock geology varies, being mainly composed of gneiss in the two specific test sites Tromsdalen and Skittenskardalen. Quaternary deposits are dominated by till and various talus deposits. Both test sites are located at

elevations from sea level to around 800 m asl and show signs of high debris flow activity. Study area 2 is located directly south-east of study area 1, with the main study location Balsfjord reaching from sea level to 1400 m asl. This study area is located more inland and experiences between 750 and 1500 mm precipitation per year and mean annual temperature between −5 and 2°C—a more continental-like climate. The geological setting in the Balsfjord area consists of mica gneiss, mica schist, gabbro and amphibolite, with quaternary deposits being dominated by thin till and various talus deposits. The area has a high debris flow activity. The Junkerdal area (3) is located in the central part of the Nordland county close to the Swedish border at elevations between 200 and 1500 m asl. The area is one of the driest in the country and has a continental climate with between 500 and 1500 mm precipitation per year and mean annual temperatures between −5 and 2°C. The Junkerdal valley is located in mica gneiss and mica schist, marble and fyllit and shows signs of high debris flow activity on the talus-draped slopes. The Nesna area (4) has mean annual temperatures between 0 and 4°C and an annual precipitation of between 2000 and 4000 mm and is located on the coast hence is directly influenced by the North Atlantic Current. The elevation range is from sea level to 800 m asl. The geology consists mostly of granites and in minor valley areas of mica gneiss and mica schist. Large portions of the landscape consist of bare bedrock and the lower slopes are in parts covered with very coarsegrained talus. Very few signs of debris flow activity are visible in this area. Study area 5 is located in the alpine fjord landscape of western Norway. Our test site at Norddal is from sea level up to about 1500 m asl and shows intermediate to low debris flow activity. Precipitation is between 1000 and 2000 mm per year and mean annual temperatures are between –3 and 4°C. Main lithologies are gneiss and occasionally ultramafic rocks and sediment cover in the slopes consists of tills or talus drapes. 3 3.1

MATERIAL AND METHODS Data

The following data sets are available and used in the present work for debris flow modeling:

Figure 1.

Overview map of the different study areas.

– A 10 m and a 25 m digital elevation model (DEM) covering all of Norway (statkart.no), and a 5 m DEM for some selected areas. – A geological map at the scale of 1:250,000.

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– A quaternary map at the scale of 1:250,000. – Detailed quaternary maps for individual sites (1:50,000). – An inventory of debris flow and landslide events (skrednett.no). 3.2

Source area identification

For the identification of possible debris flow source areas, we use an index based approach, which considers so far the three topographic characteristics slope angle, planar curvature and upslope catchment area for each DEM cell. All three parameters are extracted from a DEM with GIS-based analyses. The upslope contributing area is used to characterize the minimal water catchment for each DEM cell. Additionally, a relationship between slope angle and upslope contributing area is used according to Rickenmann & Zimmermann (1993) and Horton et al. (2008). A cell is considered as a start cell when it fulfills the threshold criterion for each parameter. Threshold values used in recent studies (Horton et al., 2008, Blahut et al., 2010) were selected and then calibrated for Norway based on an event database, field investigations, and orthophoto and detailed quaternary map analyses. 3.3

Runout and spreading modeling

For the runout modeling we used the Flow-R model (Horton et al., 2008). This model combines probabilistic and energetic algorithms for the assessment of the spreading of the flow and the maximum runout distances. The modeling performed is based on the defined source areas and the DEM. Starting from each selected source area, the possible flow paths are calculated in 3 × 3 windows by multiple-flow-direction algorithms. A weighting of the directions is included to take into account the persistence of the debris flow, using the approach of Holmgren (1994). The algorithms for the estimation of the runout distance are basic energy-based calculations, which are based on an average slope angle criterion and a kinetic energy limit. The source volume is unknown for such large-area modeling, so the runout distance calculation is based on a unit energy balance. More detailed descriptions of the model algorithms can be found in Horton et al. (2008).

4

MODELING OF SOURCE AREAS

Figure 2. Influence of varying quality of the 10 m DEM on source area detection (grey points).

The results were compared with detailed quaternary maps available for some specific areas. In general, the results obtained with a DEM at 10 m resolution or better show a good match between the modeled source areas and the debris flow tracks mapped in the quaternary maps. Results obtained with a 25 m DEM seem to be too coarse and miss a large number of the observed source areas. The 10 m DEM has been available for all of Norway since 2011. However, this 10 m DEM is developed from different basis datasets. In inhabited valley areas, high-resolution basis data sets (5 m) are available up to 600 m asl, but for higher elevations and remote areas, the new 10 m DEM is only an interpolation of the existing 25 m or even 50 m DEMs. Therefore, we encounter in areas with the low-quality data similar problems as with the 25 m DEM. A distinct, almost horizontal line is visible on the shaded DEM in the middle of Figure 2 (indicated by arrows), showing the boundary between two different basis data sets used for the DEM compilation. When using the same threshold parameters over the entire area, most potential source areas are detected in areas with the high-quality DEM, whereas a large number of the observed source areas for debris flows are missed in the areas with the interpolated 25 m DEM. These large differences in DEM quality and related variations in source area detection have to be considered for the definition of the threshold values and the composition of a susceptibility map, as an underestimation of source areas might lead to substantial underestimation of debris flow affected areas.

4.1 Influence of DEM resolution and quality The index approach used for the detection of source areas and the runout model approach was tested using DEMs at 5 m, 10 m and 25 m resolution.

4.2

Parameter calibration at different sites

Parameter calibration and verification of the modeled source areas was performed for selected sites

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in our five study areas based on quaternary maps, orthophoto analyses and fieldwork. We had to specify the threshold values for both confined and unconfined debris flows, as they are both common in Norway. In channeled topography, the model approach proves to be very robust against parameter variations and detects most of the channels being potential debris flow channels. Modeling for open slope topography, though, is strongly dependent on a precise parameterization of the model in order to detect potential source areas. Threshold values used in debris flow studies in the central European Alps are also based on a 10 m DEM are −2/100 m−1 for the curvature and 1 ha for the contribution area (Horton et al., 2008, Blahut et al., 2010). Using these values in Norway, only source areas for large channelized events were captured. Our parameter testing for both confined and unconfined debris flows in Norway has shown that feasible curvature threshold values are between −1.5/100 m−1 and −0.5/100 m−1 and contributing area values between 0.3 and 1 ha. Dahl et al. (1983) investigated slope angle values in the source areas of around 100 landslide events in Norway. Slope angles for grass land were 25–41° (mode at 37°), and for forest areas 25–47° (mode at 39°). Based on these results together with our field and orthophoto analyses, we chose 25° as lower slope angle threshold and 45° as upper slope angle threshold. Figures 3 and 4 show modeled source areas for two test sites using the same parameter set of −0.5/100 m−1 for the planar curvature, 0.5 ha for the contributing area and slope angles from 25°–45°. The Junkerdalen area is located in mica gneiss and mica schist, marble and fyllit, and shows a large number of tracks of both, confined and unconfined debris flows on the talus-draped slopes. All mapped tracks are identified by the modeling and some more source areas on scree slopes are depicted by the model, where a debris flow release is considered highly feasible (Fig. 3). In the Nesna area (Fig. 4), which is mainly granitic bedrock, only a few channelized debris flow tracks are visible on the coarse grained talus deposits, even though the model identified a large number of possible source areas. The comparison shows that this parameter set fits well the observed tracks and source areas in the Junkerdalen area, but greatly overestimates the source areas in Nesna. Hence, the same threshold values cannot be used for all of Norway. Instead, specific parameter values must be defined for different geological, morphological, and climate regions with varying debris flow activity. For a closer look at the substratum type at the modeled source areas, we analyzed and classified them from the orthophotos. Figure 5 shows the

Figure 3. Source area modeling for the Junkerdalen study area (study area 3).

Figure 4. Source area modeling for Storvatnet, Nesna area (study area 4). The approximate extent of the upper photograph is marked with white lines in the lower orthophoto.

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distribution of the substratum type of modeled source areas in the five categories. It shows clearly that for the Nesna area, over 55% of the modeled source areas are located on bare bedrock, where events rarely start, whereas in Junkerdalen most of the sources are located in soil and/ or scree slopes. This comparison shows that the detection of source areas cannot be only based on topographic parameters: Information on lithology, quaternary geology and the substratum type, hence corresponding debris flow activity, must also be incorporated. 4.3

Implementation of further criteria

Debris flow activity varies greatly between the different test sites. Furthermore, one main factor influencing this variability has been found to be the geological setting as it pertains to both bedrock and quaternary deposits. The bedrock type largely defines the weathering type and intensity, thus influencing the loose sediment availability,

type and grain sizes, which in turn controls the disposition for a debris flow. The lithology, type, and distribution of quaternary deposits on the slopes, such as till or talus, is also decisive for debris flow sensitivity. Therefore, information about the geological setting should be included in order to increase the reliability and accuracy of modeled source areas. However, the available geological data sets are on a scale of 1:250,000 for national coverage and not homogenous over the entire country. The same limitations exist for the available quaternary maps, which would be favorable to distinct between bare bedrock, sediments/soils with low disposition and sediments/soils with high disposition for failure. Nevertheless, we are working on an approach to use the available geological datasets in conjunction with remote sensing analyses to define regions with different debris flow activity in Norway and fit the model parameters accordingly. 5

Figure 5. Substratum type of modeled source areas, using two different parameter sets per test site.

Figure 6.

RUNOUT MODELING

Runout modeling has been performed for the areas where we have the source areas modeled. Parameter calibration and model validation for the runout modeling has been performed for the different test sites. The model calibration performed was based on orthophotos, detailed quaternary maps and an event database. Additionally, field work was completed for model checks. Figure 6 shows debris flow runout modeling for the Balsfjord area (study area 2). A detailed quaternary map was used here for the model calibration,

Example of runout modeling in the Balsfjord area (study area 2).

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but such maps with precise information on debris flow deposits are available only for some areas. Furthermore, the quaternary deposits often have been modified or removed by human activities, leading to a lack of information about location and maximum runout distance of past events. Nevertheless, by using a combination of all available information sets, a reasonably robust and adequate parameterization could be performed for the runout modeling. The maximum threshold used for kinetic energy, limits the debris flow energy to reasonable values. For Norway, no measurements on debris flow velocities are available, thus we considered observed velocities of various debris flow events in Switzerland, which were 13 to 14 ms−1 (Rickenmann & Zimmermann, 1993). The chosen threshold is a maximum velocity of 15 ms−1. In the used modeling approach, the probable maximum runout is characterized by a minimum travel angle, which was set to 11° (Huggel et al., 2002). This travel angle describes the slope between the starting and the end point of the debris flow. The debris flow runout modeling in Figure 6 well reproduces the debris flows events recorded on skrednett.no. The event located in the eastern part of the map is the only one not covered by the runout modeling. Our model parameterization is for debris flows, but this event was more a debris flood event, which can reach larger runout distances and stop at much lower travel angles. Runout modeling calibration proved to be easier than for the source area detection because more mapped information was available to allow quantitative comparisons. Furthermore, there are no extensive differences in runout behavior and distance in the different study areas observed. The DEM quality also plays an important role for the runout modeling. Runout modeling based on the 25 m DEM often leads to a lateral increase of the affected area, but has a minor influence on the actual runout distance. The modeling for a susceptibility map has to be conservative to include all potentially affected areas, therefore inaccuracies due to lower DEM quality has a minor importance. 6

CONCLUSION

A good correlation between the modeling results and the field observations was found, although the approach presented some limitations and cannot reflect all local controlling factors and specific conditions. However, to reach a better agreement, the threshold value setting for the source area detection has to be adapted for different regions. We therefore have to refine and enhance the method to achieve a nationwide modeling. The last

step of finalizing the national susceptibility map is to define different regions in Norway with similar lithological and morphological pattern and similar debris flow activity. The analyses in the different study areas have shown that areas with gneissic rock types are more prone to debris flows than areas covered by granitic rock types. In the Nesna area, which is representative of many coastal regions in Norway, large areas of gently undulating bare bedrock dominate the landscape and appropriate soil and debris material are not available for extensive debris flow activity. The geological settings in Troms, Balsfjord and Junkerdalen, on the other hand, favor the accumulation of debris and soil material. These areas show similar debris flow characteristics, although climatic conditions vary significantly. The following steps must be taken so that nationwide debris flow modeling can become the basis for susceptibility mapping: – Definition of different regions with similar debris flow settings allowing specific model calibration. – Consideration of the lithological setting and the quaternary deposits for parameterization of the source area modeling. – Combination with an approach for debris flood modeling (in collaboration with the Norwegian Water and Energy Resources Directorate). Extra considerations must be placed in the final parameterization, and calibration of both source area and runout modeling, because the final susceptibility map has to cover all areas potentially affected by an event with a 1/1000-years probability to comply with the building law of Norway. This demands quite conservative modeling. However, an excessive overestimation of affected areas has also to be avoided, as this susceptibility map will impact future land use planning. ACKNOWLEDGEMENTS The authors thank T. Testud for the collaboration in the data analyses and the Norwegian Water and Energy Resources Directorate (NVE) for funding this study. REFERENCES Blahut, J., Horton, P., Sterlacchini, S. & Jaboyedoff, M. 2010. Debris flow hazard modeling on medium scale: Valtellina di Tirano, Italy. Nat. Hazards Earth Syst. Sci. 10: 2379–2390.

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