No abstract

FLATModel is a two-dimensional shallow-water approximation code with corrections and modifications that create a simulation tool adapted to debris-flows behaviour. FLATModel uses the finite volume method with the numerical implementation of the Godunov scheme and includes correction terms regarding the effect of flow over high slopes and curvature. Additionally, the stop-and-go phenomenon, the basal entrainment and a correction regarding the front inclination of the final deposit are incorporated into FLATModel. In addition, different flow resistance laws were integrated in the numerical code including Bingham, HerschelBulkley and Voellmy fluid model. Firstly, our numerical model was validated using analytical solutions of a dam-break scenario and published data on a laboratory experiment. Secondly, three real events, which occurred in the northeastern part of the Iberian Peninsula, were back-calculated. Although field observations of the three events are not very detailed, the back-analyses revealed interesting patterns on the flow dynamics, and the numerical results generally showed good agreement with field data. Comparing the different flow resistance laws, the Voellmy fluid model presents the best behaviour regarding both the flow behaviour and the deposit characteristics. Preliminary simulation runs incorporating the effect of basal entrainment offered satisfactory results, although the final volume is rather sensitive on the selected friction angle of channel-bed material. The outcomes regarding the correction of the calculated front inclination of the final deposit showed that this implementation strongly improves the simulation results and better represents steep fronts of final deposits. Governing equationsThe general classification of FLATModel can be summarised as a monophasic, 2D model with the z-axis normal to the bed. As in other models, the development of the 2D-governing equations Original ArticleLandslides 5 • (2008) 127

Abstract. Real-time assessment of debris-flow hazard is fundamental for developing warning systems that can mitigate risk. A convenient method to assess the possible occurrence of a debris flow is to compare measured and forecasted rainfalls to critical rainfall threshold (CRT) curves. Empirical derivation of the CRT from the analysis of past events' rainfall characteristics is not possible when the database of observed debris flows is poor or when the environment changes with time. For debris flows and mud flows triggered by shallow landslides or debris avalanches, the above limitations may be overcome through the methodology presented. In this work the CRT curves are derived from mathematical and numerical simulations, based on the infinite-slope stability model in which slope instability is governed by the increase in groundwater pressure due to rainfall. The effect of rainfall infiltration on landside occurrence is modelled through a reduced form of the Richards equation. The range of rainfall durations for which the method can be correctly employed is investigated and an equation is derived for the lower limit of the range. A large number of calculations are performed combining different values of rainfall characteristics (intensity and duration of event rainfall and intensity of antecedent rainfall). For each combination of rainfall characteristics, the percentage of the basin that is unstable is computed. The obtained database is opportunely elaborated to derive CRT curves. The methodology is implemented and tested in a small basin of the Amalfi Coast (South Italy). The comparison among the obtained CRT curves and the observed rainfall amounts, in a playback period, gives a good agreement. Simulations are performed with different degree of detail in the soil parameters characterization. The comparison shows that the lack of knowledge about the spatial variability of the parameters may greatly affect the results. This problem is partially mitigated by the use of a Monte Carlo approach.

No abstract

No abstract

No abstract

Landslides falling into water bodies can generate impulsive waves, which are considered as tsunamis. The propagating wave may be highly destructive for hydraulic structures, civil infrastructure and people living along the shorelines. A facility to study this phenomenon was set up in the laboratory of the Technical University of Catalonia. The setup consists of a new device releasing granular material at high velocity into a wave basin. A system employing laser sheets, high-speed and high-definition cameras was designed to accurately measure the high velocity and geometry of the sliding mass as well as the produced water displacement in time and space. The analysis of experimental data helped to develop empirical relationships linking the landslide parameters with the produced wave amplitude, propagation features and energy, which are useful tools for the hazard assessment. The empirical relationships were successfully tested in the case of the 2007 event that occurred in Chehalis Lake (Canada).

Based on debris-flow inventories and using a geographical information system, the susceptibility models presented here take into account fluvio-morphologic parameters,\ud gathered for every first-order catchment. Data mining techniques on the morphometric\ud parameters are used, to work out and test three different models. The first model is a\ud logistic regression analysis based on weighting the parameters. The other two are classification trees, which are rather novel susceptibility models. These techniques enable gathering the necessary data to evaluate the performance of the models tested, with and without optimization. The analysis was performed in the Catalan Pyrenees and covered an area of more than 4,000 km2. Results related to the training dataset show that the optimized models performance lie within former reported range, in terms of AUC, although closer to\ud the lowest end (near 70 %). When the models are applied to the test set, the quality of most results decreases. However, out of the three different models, logistic regression seems to offer the best prediction, as training and test sets results are very similar, in terms of performance. Trees are better at extracting laws from a training set, but validation through a test set gives results unacceptable for a prediction at regional scale. Although omitting\ud parameters in geology or vegetation, fluvio-morphologic models based on data mining, can\ud be used in the framework of a regional debris-flow susceptibility assessment in areas where only a digital elevation model is available.Postprint (published version