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In recent years, photogrammetric models have gained widespread use in geosciences due to their ability to reproduce natural surfaces. These models offer a cost-effective and user-friendly alternative to other systems, such as LiDAR, for creating 3D point clouds. On the other hand, rockfalls pose a significant risk to society, as they are the most common natural hazard in mountainous areas and can occur with great speed, resulting in high levels of danger. The aim of this communication is to show results on the development of new algorithms and time-lapse photogrammetric systems for automatic rockfall monitoring (Blanch, 2022).To acquire the data, a photogrammetric system consisting of different photographic modules and a data transmission module has been developed. This system uses conventional cameras (24Mpx-48Mpx) powered by solar panels and it is controlled by a Raspberry Pi. The system captures time-lapse images, can be programmed, configured flexibly, and it can send images remotely for near real-time processing. The system has been installed at two sites with rockfall activity. One in the Puigcercós cliff, located in the Origens UNESCO Gobal Geopark (Spain), and the other in the Tajo de San Pedro cliff located in the Alhambra de Granada - UNESCO World Heritage Site (Spain).Data processing comprises two main steps. The first step involves the automatic photogrammetric process using SfM-MVS algorithms. Thereby, the MEMI workflow is applied to improve the level of detection in the change-detection comparison (Blanch et al., 2021). Afterwards, a workflow based on M3C2 (Lague et al., 2013) comparison and DBSCAN clustering is applied to identify possible rockfall clusters. The resulting clusters are processed via a machine learning approach to automatically discriminate the true rockfall events from the candidate clusters . To perform this task, various metric parameters, i.e. features, of the candidate clusters are calculated, and a Random Forest machine learning model is used to perform the classification.The presented approach facilitates the automated monitoring of rockfalls in near-real time, while improving the detection threshold in the 3D change-detection models, resulting in a more detailed characterisation of active zones and defining the framework that allows for automated 4D rockfall monitoring in high temporal frequency.Blanch, X., 2022. Developing Advanced Photogrammetric Methods for Automated Rockfall Monitoring. Doctoral dissertation. http://hdl.handle.net/10803/675397Blanch, X.; Eltner, A.; Guinau, M.; Abellan, A., 2021. Multi-Epoch and Multi-Imagery (MEMI) Photogrammetric Workflow for Enhanced Change Detection Using Time-Lapse Cameras. Remote Sens. , 13, 1460. https://doi.org/10.3390/rs13081460Lague, D., Brodu, N., Leroux, J., 2013. Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z). ISPRS Journal of Photogrammetry and Remote Sensing 82, 10–26. https://doi.org/10.1016/j.isprsjprs.2013.04.009

The training received during primary and secondary education is crucial for the development of our behavior in adulthood. For this reason, at this early stage, it is important to know our environment and its dynamics to appreciate it. The Spanish Secondary Compulsory Education (SCE) comprises four academic years and is taken by students aged 12-16. The learning of geosciences during this level takes place only in the first, third and fourth year. Biology and geology are part of the same subject, being mandatory in the first and third year and optional in the last year. The students lose their interest in science as they advance from primary to secondary education and cease to see it as a viable option for their future (Zamalloa and Sanz, 2020). Several authors have also detected this lack of interest in science of young people (Ali et al., 2013; Christidou, 2011). This disinterest is manifested in expressions such as "Science does not provide food", "You have to study hard" or "Science is boring". To avoid this lack of motivation, educational models should be reoriented towards an educational system in which students are at the center of their learning and not simply focused on the systematic acquisition of memorized concepts to pass an exam. The discipline of Earth Sciences, or Geosciences, face a more difficult challenge than many other sciences given that students do not learn geology during early stages of their academic life (Adetunji et al., 2012). In many places such as the USA (Lewis, 2008), Australia (Dawborn-Gundlach et al., 2017), some countries of northern Europe (King, 2013), Argentina and other Latin American countries (Lacreu 2017; UNESCO, 2019) and Spain (Pedrinaci, 2012) geoscience subjects are underrepresented and frequently in the optional subjects, it is selected by students who are unmotivated. Given this context, the present study investigates the structure of geoscience education in the frame of the SCE system from Catalonia and the degree of geological knowledge acquired by the students.

The pandemic situation we are experiencing has forced us to transform face-to-face teaching into virtual teaching. Digital platforms hinder the interaction, discussion and feedback that naturally occur in a face-to-face class, but at the same time, they provide an opportunity to put the focus on the student’s learning rather than on content delivering. Learning include both, inductive and deductive processes; induction can be effectively acquired by using case studies; then, deduction can be achieved through comparison, analysis, generalisation and synthesis. Digital platforms appear as an optimal resource to facilitate the individual and collaborative tasks and learning processes. In this work we present our experience on the landslide hazard subject (Master’s level) focussed on the student’s learning through the use of digital media.Internet information of undeniable quality that can be easily accessed is basic: The Landslide Blog by Dave Petley (https://blogs.agu.org/landslideblog/) in Blogosphere hosted by AGU (American Geophysical Union) provides valuable and updated information on landslide events occurring worldwide. The learning activities are structured around several cases selected by the lecturer from the blog to ensure the analysis of the most frequent landslide types. All activities are developed in 8 steps: 1) The teacher presents the learning action (objective, tasks, and assessment guide) using a Genially platform interactive image; 2) Each student selects one of the proposed cases and compile relevant information about it; 3) Each student analyses the landslide characteristics, identifies the landslide type and classifies it according to Hungr et al., 2014 (available through the educational virtual platform), and recognises the control and triggering factors (one virtual session is programmed and a forum tool is provided to the students to discuss and to solve doubts); 4) Each student selects and organizes the significant information about each case by building an interactive image in Genially; 5) Each student presents each case using his/her interactive image in a virtual session, which is recorded and uploaded to the educational platform; 6) Students peer evaluate the content and design of the interactive images and oral presentations based on the provided assessment guide; 7) During a predetermined time, students collaboratively compile all the information in a Google sheet table to synthesize the geomorphological characteristics, materials involved, mobilization mechanisms and control and triggering factors of the different types of landslides; 8) the synthetic table is discussed and completed during a virtual session.All the knowledge and skills acquired by students with these activities are put into practice in a two-day field trip where students have to identify, characterize and classify different types of landslides as well as their control and triggering factors. The risk situation and the mitigation strategies are discussed in each case and compared to the ones studied through virtual learning. Furthermore, students get used and learn how to clearly present information through virtual tools, as Genially, useful for dissemination purposes.Hungr et al. 2014. The Varnes classification of landslide types, an update. Landslides 11(2). DOI: 10.1007/s10346-013-0436-y

Seismic sensors installed in areas prone to rockfalls provide a continuous record of the phenomenon, allowing real-time detection and characterization. Detection of small scale rockfalls (< 0.001 m3), that might be precursors of larger events, can be worthwhile for early warning systems of rockfalls. However, seismic signals are closely dependent on the characteristics of the event and on the geotechnical characteristics of the ground, making the detection of small rockfalls complex and requiring detailed in-situ analyzes. For this reason, an experiment was carried out on the UB experimental site (Puigcercós Cliff, Catalonia, NE Spain) on 6th-7th of June 2013, where 21 rocks with volumes ranging from 0.0015 m3 to 0.0004 m3 were thrown from the top of the cliff (200 m long and 27 m high) and the seismic signals were registered with three 3D short period seismic sensors located at different distances from the rock wall: 57 m, 67 m, and 107 m.The recorded seismic signals have a frequency content between 10-30 Hz, and the duration of the peak amplitudes varied between 0.3 and 0.6 s. Based on these characteristics, different phases of the dynamics of the rockfalls were identified, including main impacts, rebounds, flights, rolling and final stop of the events. The furthest station recorded the lowest frequency and amplitude values, limiting our ability to detect those blocks smaller than 0.0015 m3. Comparing the results with the nearest station, seismic attenuation phenomena is detectable even at distances of 50 m.After the experiment, a permanent seismic station was installed in the area, at 107 m from the cliff. Using LiDAR and 2D imagery monitoring, two naturally triggered rockfalls were identified on 30th and 31st August 2017 (0.28 m3 and 0.25 m3 respectively). Based on the results from the experiment and an automatic detection system, these main events and prior minor events have been found in the continuous seismic records of this permanent station. The characteristics of these natural detachments differ partially from the artificially triggered rockfalls during the experiment, since the geometry of the seismic signals is different. The observed shapes of the natural detachments are similar to that of granular flows, much more continuous than the sharp shapes that were observed in the isolated blocks of the experiment. This shows the possibility of incorporating seismic stations for the automatic detection and initial characterization of rockfalls and its effectiveness in detecting frequencies of occurrence.In order to evaluate the possibility of estimating rockfall volumes, diverse energy ratios (Es/Ep) were calculated. However, precise volume estimation is not possible. Nevertheless, the combination of seismic data with LiDAR and photographic techniques allows accurate new volume calculations of rockfalls to be incorporated progressively into the study of rockfalls.ACKNOWLEDGMENTS: The authors would like to acknowledge the financial support from CHARMA (CGL2013-40828-R) and PROMONTEC (CGL2017-84720-R AEI/FEDER, UE) projects, Spanish MINEICO. We are also thankful to Origens UNESCO Global Geopark.

Rockfall detection is a crucial procedure in the field ofgeology, which helps to reduce the associated risks.Currently, geologists identify rockfall events almostmanually utilizing point cloud and imagery data ob-tained from different caption devices such as TerrestrialLaser Scanner (TLS) or digital cameras. Multitemporalcomparison of the point clouds obtained with thesetechniques requires a tedious visual inspection to iden-tify rockfall events which implies inaccuracies that de-pend on several factors such as human expertize and thesensibility of the sensors. This paper addresses this issueand provides an intelligent framework for rockfall eventdetection for any individual working in the intersectionof the geology domain and decision support systems.The development of such an analysis framework pre-sents major research challenges and justifies exhaustiveexperimental analysis. In particular, we propose an in-telligent system that utilizes multiple machine learningalgorithms to detect rockfall clusters of point cloud data.Due to the extremely imbalanced nature of the problem,aplethoraofstateoftheart resampling techniques ac-companied by multiple models and feature selectionprocedures are being investigated. Various machine learning pipeline combinations have been examinedand benchmarked applying wellknown metrics to beincorporated into our system. Specifically, we developedmachine learning techniques and applied them to ana-lyze point cloud data extracted from TLS in two distinctcase studies, involving different geological contexts: thebasaltic cliff of Castellfollit de la Roca and the con-glomerate Montserrat Massif, both located in Spain. Ourexperimental results indicate that some of the abovementioned machine learning pipelines can be utilized todetect rockfall incidents on mountain walls, with ex-perimentally validated accuracy.

The emerging use of photogrammetric point clouds in three-dimensional (3D) monitoring processes has revealed some constraints with respect to the use of LiDAR point clouds. Oftentimes, point clouds (PC) obtained by time-lapse photogrammetry have lower density and precision, especially when Ground Control Points (GCPs) are not available or the camera system cannot be properly calibrated. This paper presents a new workflow called Point Cloud Stacking (PCStacking) that overcomes these restrictions by making the most of the iterative solutions in both camera position estimation and internal calibration parameters that are obtained during bundle adjustment. The basic principle of the stacking algorithm is straightforward: it computes the median of the Z coordinates of each point for multiple photogrammetric models to give a resulting PC with a greater precision than any of the individual PC. The different models are reconstructed from images taken simultaneously from, at least, five points of view, reducing the systematic errors associated with the photogrammetric reconstruction workflow. The algorithm was tested using both a synthetic point cloud and a real 3D dataset from a rock cliff. The synthetic data were created using mathematical functions that attempt to emulate the photogrammetric models. Real data were obtained by very low-cost photogrammetric systems specially developed for this experiment. Resulting point clouds were improved when applying the algorithm in synthetic and real experiments, e.g., 25th and 75th error percentiles were reduced from 3.2 cm to 1.4 cm in synthetic tests and from 1.5 cm to 0.5 cm in real conditions.

More than a century ago, the Puigcercós village located in the region of Pallars Jussà (Catalonia, Spain), suffered a large-scale landslide that occurred on January 13th, 1881. More than 5 million m3 of sediments and rocks were displaced and a 200 m long and 25 m high rock scarp was formed. Luckily, during the main event, the nearby village was not affected, and due to a prompt evacuation and re-location of the entire village, no casualties were reported. Nevertheless, consequent retreat of the main scarp did destroy the big part of the old village, which confirmed not only the necessity for its relocation, but also gave one of the first clearly described and confirmed examples of a successful geologic risk prevention.During the last decade, the members of the RISKNAT-UB group have chosen this site to conduct pilot studies of rockfalls and landslides using a multidisciplinary approach. The utilized observational techniques include Terrestrial Laser Scanner (TLS), photogrammetry, GPS, seismic monitoring and geophysical prospecting techniques. The work presented here is an overview of these activities, including the main milestones of the ongoing research. Special emphasis will be given to the use of geodetic techniques for investigating changes on the depositional area of the landslide and around the crown cracks at the upper level of the main scarp. As a result of the GPS observations, for the first time, 130 years after the occurrence of the event, it was possible to observe a continuing geomorphological activity of the depositional zone of this historical landslide, Currently, the RISKNAT-UB group operates cost-effective, high-resolution and low-cost photogrammetric instruments and seismic continuous records at the site, in order to monitor the evolution of the Puigcercós rock scarp. The correlation of the seismic and the photogrammetric data and intermittently obtained LiDAR images enables us to monitor and characterize frequent rockfalls and premonitory deformations occurring at the site. These observations have allowed quantifying the rate of retreat of the rock scarp at a rate of 10 to 11 cm/yr and a slow motion of the depositional zone up to 6 mm/yr. Since the geologic risk at the study area is not significant, due to the absence of population and/or infrastructures, this site is an ideal natural laboratory for developing new observational techniques, which can be used to develop early warning systems for rockfalls and landslides.The authors would like to acknowledge a financial support from CHARMA (CGL2013-40828-R) and PROMONTEC (CGL2017-84720-R AEI/FEDER, UE) projects, financed by the Spanish MINEICO. We are also thankful to UNESCO Global Geopark Conca de Tremp-Montsec for their support.

In recent years, different techniques and devices (LIDAR, photogrammetry, UAVs or hyperspectral sensors….) have been used to acquire large amounts of data for the study of the earth’s surface offering high temporal, spatial and spectral resolutions. However, a problem lies on the availability of an efficient methodology to extract the desired information with geological signification from these large datasets. Minimal intervention of the experienced users and automatic or semi-automatic data processing are mandatory to avoid dilatory processes and to obtain productive results.Our aim is to develop a new methodology for the identification and classification of changes in the surface of cliffs from consecutive point clouds. The new algorithms implemented recognize the different orientations of the point cloud and then, compare each point respect to a previous one in the normal direction isolating clusters of displaced points. Thereafter, these clusters of points are classified according to geometrical and raw data parameters in a) rockfalls, b) small movements of the rock surface and c) non-interest clusters of vegetation or noise like edge effects. The methodology is focused on creating more geometrical features which serve as criteria to identify and classify the differences between two point clouds. Actually, the number of clusters remains slightly high for manual processing. In this regard, the aim is to minimize the interaction of the user and take advantage of the large volume of data generated from high temporal resolution associated with the monitoring. The high number of events collected along years of monitoring allows the use of Machine Learning techniques to improve the classification of clusters automatically.Montserrat Massif (Catalonia, Spain) is a singular case study of rockfall risk to apply the developed methodology due to the high presence of visitors, whose security conflicts with natural heritage preservation. For a correct design of infrastructures protection measures, a rockfall monitoring plan is under development including Terrestrial Laser Scanner from 2007.

We present our experience of learning by doing while integrating ICT, advanced and classic techniques in classroom and fieldwork, to teach Natural Hazards at University. The learning activities are structured around a mountain valley affected by floods and landslides. The working groups (4-5 students) focus on a specific stretch along the valley and adjacent slopes. We alternate classroom activities with field work, organized in 3 steps: 1) Information compilation and preparation of the field work; 2) field work (3 days); and 3) GIS analysis considering hazardous and exposed areas, and final synthesis. The students work with dropbox to compile basic hazard information of the area from archives, administration and university databases, etc. To prepare the field work, they use classical stereoscopic photointerpretation to characterize the geoforms and to study the reference flood occurred in 1982 through ancient aerial photographs. In the field, they use cellphone photos, GoogleEarth and PPT to present their preliminary observations. They use these data to collectively construct the indicators and hazard legends, and to generate the indicators and hazard maps (group task). To conclude, the students present an individual report, including a hazard evaluation and a preliminary risk map generated by GIS analysis. The group tasks and the individual report are used for assessment.

The sensitive mountain catchment of Portain&eacute, (Eastern Pyrenees, Iberian Peninsula) has recently experienced a significant change in its torrential dynamics due to human disturbances. The emplacement of a ski resort at the headwaters led to the surpassing of a geomorphological threshold, with important consequences during flood events. Consequently, since 2008, channel dynamics have turned into sediment-laden, highly destructive torrential flows. In order to assess this phenomenon and to acquire a holistic understanding of the catchment&rsquo, s behaviour, we carried out a field work-based multidisciplinary study. We considered the interaction of the various controlling factors, including bedrock geology, geomorphological evolution, derived soils and coluvial deposits, rainfall patterns, and the hydrological response of the catchment to flood events. Moreover, anthropogenic land-use changes, its consequential hydrogeomorphic effects and the role of vegetation were also taken into account. Robust sedimentological and geomorphological evidence of ancient dense debris flows show that the basin has shifted around this threshold, giving rise to two different behaviours or equilibrium conditions throughout its history: alternating periods of moderate, bedload-laden flows and periods of high sediment-laden debris flow dynamics. This shifting could have extended through the Holocene. Finally, we discuss the possible impact of climate and global change, as the projected effects suggest future soil and forest degradation, this, jointly with more intense rainfalls in these mountain environments, would exacerbate the future occurrence of dense sediment-laden flows at Portain&eacute, but also in other nearby, similar basins.

Multi-temporal digital elevation models (DEMs) obtained from airborne LiDAR surveys are widely used to detect geomorphic changes in time and quantify sediment budgets. However, they have been rarely applied to study the geomorphic impact of engineering structures in mountain settings. In this study, we assessed the influence and behavior of flexible sediment retention barriers in the Portaine catchment (Spanish Pyrenees), using three LiDAR data sets (2009, 2011 and 2016) that covered a 7-year period. Densely forested mountainous areas present some limitations for reliable DEM analysis due to spatial variabilities in data precision, accuracy and point density. A new methodological approach for robust uncertainty analysis along channels, based on changes in cross-sectional elevations, was used to discriminate noise from real geomorphic changes. The obtained results indicated that erosion occurs along most reaches covering a large area, whereas deposition is localized in specific areas such as those upstream of sediment retention barriers and in the debris cone. Despite the presence of 15 flexible sediment retention barriers, the channels presented net degradation during both 2009–2011 and 2011–2016, with 2838 and 147 m3 of material exported from the basin, respectively. For the same periods, the barriers retained 33% and 25% of the total deposition (up to 1300 m3 per barrier), respectively, but also induced lateral and downstream incision, the latter reaching 703 m3 for a single barrier. We detected a horizontal displacement of the net of up to 1.2 m in filled barriers, resulting from net flexion. The interference of the natural river evolution by defense measures has resulted in a complex erosion-deposition pattern. The presented methods show high potential for the hydrogeomorphic study of mountain catchments, especially for a high-resolution assessment of flexible barriers or other engineering structures in remote areas.

Slope failures have been traditionally detected by field inspection and aerial-photo interpretation. These approaches are generally insufficient to identify subtle landforms, especially those generated during the early stages of failures, and particularly where the site is located in forested and remote terrains. We present the identification and characterization of several large and medium size slope failures previously undetected within the Orri massif, Central Pyrenees. Around 130 scarps were interpreted as being part of Rock Slope Failures (RSFs), while other smaller and more superficial failures were interpreted as complex movements combining colluvium slow flow/slope creep and RSFs. Except for one of them, these slope failures had not been previously detected, albeit they extend across a 15% of the studied region. The failures were identified through the analysis of a high-resolution (1 m) LIDAR-derived bare earth Digital Elevation Model (DEM). Most of the scarps are undetectable either by fieldwork, photo interpretation or 5 m resolution topography analysis owing to their small heights (0.5 to 2 m) and their location within forest areas. In many cases, these landforms are not evident in the field due to the presence of other minor irregularities in the slope and the lack of open views due to the forest. 2D and 3D visualization of hillshade maps with different sun azimuths provided an overall picture of the scarp assemblage and permitted a more complete analysis of the geometry of the scarps with respect to the slope and the structural fabric. The sharpness of some of the landforms suggests ongoing activity, which should be explored in future detailed studies in order to assess potential hazards affecting the Portaine ski resort. Our results reveal that close analysis of the 1 m LIDAR-derived DEM can significantly help to detect early-stage slope deformations in high mountain regions, and that expert judgment of the DEM is essential when dealing with subtle landforms. The incorporation of this approach in regional mapping represents a great advance in completing the catalogue of slope failures and will eventually contribute to a better understanding of the spatial factors controlling them.

The Portainé mountain catchment, containing the Port Ainé ski resort (Lleida, Spanish Pyrenees), displays active erosional and depositional phenomena caused by periodic torrential floods. These events present a potential risk and incur significant economic losses. In ungauged remote catchments (like Portainé), trees might be the only paleohydrological source of information regarding past floods. Thus, we estimated the temporal and spatial distribution of torrential floods by dendrogeomorphological techniques to assess whether human impact (land-use changes and infrastructure works) affected their frequency and magnitude. One-hundred and sixty-six samples from 67 trees belonging to 10 different species were analyzed; past flood events of the last 50 years were identified by dating and relating evidence between them. Moreover, a detailed geomorphological study was performed and the available historical data compiled. Our multi-evidence analysis provides new insight into the occurrence of paleofloods. Changes in flood frequency since 2006, especially from 2008, suggest that the geomorphological equilibrium has been disturbed, coinciding with both major earthworks within the ski resort and intense but not extraordinary rainfall. This conclusion has important implications for land planning and the design of future projects in the mountain watersheds = La cuenca de montaña de Portainé, donde se sitúa la estación de esquí de Port Ainé (Lleida, Pirineo español), presenta fenómenos erosivos y deposicionales activos debido a periódicas avenidas torrenciales que suponen un riesgo potencial y producen pérdidas económicas significativas. En las cuencas no aforadas remotas, como Portainé, los árboles pueden ser la única fuente de información paleohidrológica y, por ello, en este trabajo se ha estudiado la distribución temporal y espacial de las avenidas torrenciales mediante técnicas dendrogeomorfológicas, evaluando si cambios en los usos del suelo y obras de infraestructura pudieron afectar a su frecuencia y magnitud. Se analizaron ciento sesenta y seis muestras de 67 árboles correspondientes a 10 especies diferentes; y mediante la datación y la relación entre las distintas evidencias, fue posible identificar avenidas pretéritas en los últimos 50 años. Además se realizó un estudio geomorfológico detallado y se recopilaron todos los datos históricos disponibles. Este análisis de evidencias múltiples proporciona una visión nueva sobre la ocurrencia de paleoavenidas, cuyos cambios en la frecuencia ocurridos desde el año 2006, y especialmente desde 2008, sugieren que el equilibrio geomorfológico se vio perturbado, coincidiendo con los movimientos de tierras en las obras de la estación de esquí y unas precipitaciones intensas, pero no extraordinarias. Esta conclusión tiene implicaciones importantes para la planificación territorial y el diseño de obras futuras en las cuencas hidrográficas de montaña., Departamento de Sistemas y Recursos Naturales, Universidad Politécnica de Madrid, España, Área de Riesgos Geológicos, Instituto Geológico y Minero de España, España, GEOMODELS Institut de Recerca, Universitat de Barcelona, España

vol.162, 216-229., Torrential floods are hazardous hydrological phenomena that produce significant economic damage worldwide. Flood reconstruction is still problematic in ungauged mountainous areas due to the lack of systematic data, so indirect techniques are required. This paper presents an integrated palaeoflood study of a Pyrenean stream that combines fluvio-torrential geomorphology, dendrogeomorphology, palaeoflood discharge estimation and flow hydraulics. The use of a total station and airborne LiDAR data allows detailed topography for geomorphological mapping and running a one-dimensional hydraulic model. Based on the height of scars on several damaged trees, we obtained palaeodischarges of 316 m3 s−1 and 314 m3 s−1 for the 2008 and 2010 floods. The hydraulic parameters were related to the geomorphic position of trees, showing a positive relation between most energetic geomorphic elements and both flow depth and velocity values. The most affected trees are located in intermediate energy geomorphic positions. Analysing variation in scar height and flow stage differences, we suggest that most reliable trees for peak discharge estimation correspond to those in areas related with fluvio-torrential processes of intermediate energy. This multidisciplinary palaeohydrological study relates flood hydrodynamics with the damage to trees and their geomorphological characteristics, focusing on the hydraulic parameters of the peak flow (depth, velocity and unit stream power), which has never been performed before. The proposed approach shows strong potential for palaeoflood analysis in ungauged mountain catchments with scarce nonsystematic data., Geomodels Research Institute, Departament d'Geodinàmica i Geofísica, Universitat de Barcelona, España, Área de Riesgos Naturales, Instituto Geológico y Minero de España, España, Departamento de Sistemas y Recursos Naturales, Universidad Politécnica de Madrid, España

Work on transversal competences in university degrees is a teaching line entirely established since the implementation of the European Credit Transfer System (ECTS). Nevertheless, undergraduate students present shortcomings in the development of some of these competences, especially on collaborative work, time management, oral and writing expression, and on the use of information resources.This paper presents a virtual tool and associated guidelines to enhance the information management during the development of collaborative works, and to facilitate the information availability among the students. Moreover, this tool and the guideline improve the individual monitoring and evaluation of the contributions of the students to the work.The proposal presented in this work belongs to a teaching innovation project carried out in the Earth Sciences Faculty of the Universitat de Barcelona, and implemented in the subjects General Geology (first-year in the Engineering Geology degree, 6 ECTS) and Geochemistry (third-year course in the Geology degree, 9 ECTS).

On May 5th, 2013 a planar rock slide (~450 m3) occurred in the village of La Riba (NE Spain), which forced the closure of the road C-240z for 6 months. This slide left a hanging block (~130 m3) suspended on the slope forcing a controlled blasting, followed by rock slope stabilization works. The volume of rock displaced during the both events was deduced from LiDAR and photogrammetry data following two approaches: subtracting pre- and post-event data and reconstructing the volume by fitting planes on the structural surfaces after a structural analysis of the slope. Information about the natural rock slide was obtained from the records of two permanent broadband seismic stations located 10 km from the site. From these seismic records, the existence of a rock slide was confirmed and its time of occurrence was determined, information that would be otherwise unknown. In addition, despite the small volume displaced during the event, its location was deduced from a single seismic station analysis. The blasting process was recorded with two high-definition (HD) video cameras and by two temporary seismic stations deployed close to the site (10 km). Partially, because only a part of the released energy is transmitted into the ground as seismic energy, and partially because the recorded seismic signal is highly dependent on the event characteristics and the geotechnical conditions of the ground materials. Nevertheless, seismic data is very well suited to detect and characterize in detail both rockfall events of different nature and size. Merging and integrating remote sensing techniques such as LiDAR or photogrammetry with seismic measurements should allow the implementation of rockfall early warning systems.

Although developing countries are vulnerable to landslide hazard, they generally lack policies for hazard assessment and mitigation. This may be attributed to the scarcity of good quality data on which to base any sound hazard assessment in addition to insufficient funds and lack of political will. Thus, there is an urgent need for developing feasible methodologies of landslide hazard assessment and mitigation, which can be readily tested and implemented under the conditions found in these countries. To this end, we selected an area of about 20 km 2 badly affected by Hurricane Mitch in October 1998, in the Departamento de Chinandega (NW Nicaragua). Mass movements (mainly debris flows) produced during the Hurricane Mitch rainfall event were investigated using two sets of aerial photographs at 1:60,000 and 1:40,000 scales. Data concerning regolith composition and thickness, landslide dimensions, failure slope angle and land use were obtained for 150 mass movements, and over 450 landslides were mapped at 1:10,000 scale in the field. A pragmatic approach was used to produce a qualitative hazard (sensu lato) assessment, based on the concepts of number of events recorded , predictability and susceptibility . This case study shows that a hazard assessment that is useful for management may be possible even where data are limited. Despite its inherent limitations, similar pragmatic approaches could help the sustainable development of other rural and sparsely populated areas of Central America.

In October 1998, Hurricane Mitch triggered a large number of landslides (mainly debris flows) in Honduras and Nicaragua, resulting in a high death toll and in considerable damage to property. In recent years, a number of risk assessment methodologies have been devised to mitigate natural disasters. However, due to scarcity of funds and lack of specialised personnel few of these methodologies are accessible to developing countries. To explore the potential application of relatively simple and affordable landslide susceptibility methodologies in such countries, we focused on a region in NW Nicaragua which was among the most severely hit during the Mitch event. Our study included (1) detailed field work to produce a high-resolution inventory landslide map at 1 : 10,000 scale, and (2) a selection of the relevant instability factors from a Terrain Units Map which had previously been generated in a project for rural development. Based on the combination of these two datasets and using GIS tools we developed a comparative analysis of failure-zones and terrain factors in an attempt to classify the land into zones according to the propensity to landslides triggered by heavy rainfalls. The resulting susceptibility map was validated by using a training and a test zone, providing results comparable to those reached in studies based in more sophisticated methodologies. Thus, we provide an example of a methodology which is simple enough to be fully comprehended by non-specialised technicians and which could be of help in landslide risk mitigation through implementation of non-structural measures, such as land planning or emergency measures.

International audience; In October 1998, Hurricane Mitch triggered numerous landslides (mainly debris flows) in Honduras and Nicaragua, resulting in a high death toll and in considerable damage to property. The potential application of relatively simple and affordable spatial prediction models for landslide hazard mapping in developing countries was studied. Our attention was focused on a region in NW Nicaragua, one of the most severely hit places during the Mitch event. A landslide map was obtained at 1:10 000 scale in a Geographic Information System (GIS) environment from the interpretation of aerial photographs and detailed field work. In this map the terrain failure zones were distinguished from the areas within the reach of the mobilized materials. A Digital Elevation Model (DEM) with 20 m×20 m of pixel size was also employed in the study area. A comparative analysis of the terrain failures caused by Hurricane Mitch and a selection of 4 terrain factors extracted from the DEM which, contributed to the terrain instability, was carried out. Land propensity to failure was determined with the aid of a bivariate analysis and GIS tools in a terrain failure susceptibility map. In order to estimate the areas that could be affected by the path or deposition of the mobilized materials, we considered the fact that under intense rainfall events debris flows tend to travel long distances following the maximum slope and merging with the drainage network. Using the TauDEM extension for ArcGIS software we generated automatically flow lines following the maximum slope in the DEM starting from the areas prone to failure in the terrain failure susceptibility map. The areas crossed by the flow lines from each terrain failure susceptibility class correspond to the runout susceptibility classes represented in a runout susceptibility map. The study of terrain failure and runout susceptibility enabled us to obtain a spatial prediction for landslides, which could contribute to landslide risk mitigation.