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Las zonas montañosas son propensas a inundaciones torrenciales que se caracterizan por su capacidad de movilizar grandes volúmenes de sedimentos y otros materiales como troncos y otras piezas de madera de gran tamaño. En el caso de la cuenca alta del río Noguera Pallaresa (Pirineos de Lleida, España), las principales inundaciones destructivas están bien identificadas y descritos sus principales parámetros, pero aún falta el detalle espacial y temporal de éstos y otros eventos menos severos. Con el objetivo de ampliar la información sobre los efectos de estas inundaciones, este trabajo analiza y compara diferentes fuentes de datos, a partir de muestreos dendrogeomorfológicos realizados en dos afluentes del río Noguera Pallaresa: el río Flamisell (en el margen derecho) y el río Romadriu (en el margen izquierdo). La datación de los principales indicadores dendrogeomorfológicos en ambos afluentes, especialmente cicatrices y cambios bruscos de crecimiento, así como las fechas estimadas de establecimiento de los árboles, se han contrastado con datos de fuentes documentales, geomorfológicas, meteorológicas e hidrológicas. Se ha comprobado que, en estas pequeñas y medianas subcuencas, con precipitaciones muy heterogéneas a causa de la orografía, los datos instrumentales (a menudo muy escasos) pueden no reflejar adecuadamente la dinámica hidrológica. Por tanto, se constata que el análisis de las fuentes dendrogeomorfológicas y documentales es fundamental para caracterizar y evaluar la incidencia de estas inundaciones torrenciales en diferentes ámbitos territoriales.

Quantitative hazard analysis of rockfalls is a fundamental tool for sustainable risk management, even more so in places where the preservation of natural heritage and people’s safety must find the right balance. The first step consists in determining the magnitude-frequency relationship, which corresponds to the apparently simple question: how big and how often will a rockfall be detached from anywhere in the cliff? However, there is usually only scarce data on past activity from which to derive a quantitative answer. Methods are proposed to optimize the exploitation of multi-source inventories, introducing sampling extent as a main attribute for the analysis. This work explores the maximum possible synergy between data sources as different as traditional inventories of observed events and current remote sensing techniques. Both information sources may converge, providing complementary results in the magnitude-frequency relationship, taking advantage of each strength that overcomes the correspondent weakness. Results allow characterizing rockfall detachment hazardous conditions and reveal many of the underlying conditioning factors, which are analyzed in this paper. High variability of the hazard over time and space has been found, with strong dependencies on influential external factors. Therefore, it will be necessary to give the appropriate reading to the magnitude-frequency scenarios, depending on the application of risk management tools (e.g., hazard zoning, quantitative risk analysis, or actions that bring us closer to its forecast). In this sense, some criteria and proxies for hazard assessment are proposed in the paper.

Rock slope monitoring using 3D point cloud data allows the creation of rockfall inventories, provided that an efficient methodology is available to quantify the activity. However, monitoring with high temporal and spatial resolution entails the processing of a great volume of data, which can become a problem for the processing system. The standard methodology for monitoring includes the steps of data capture, point cloud alignment, the measure of differences, clustering differences, and identification of rockfalls. In this article, we propose a new methodology adapted from existing algorithms (multiscale model to model cloud comparison and density-based spatial clustering of applications with noise algorithm) and machine learning techniques to facilitate the identification of rockfalls from compared temporary 3D point clouds, possibly the step with most user interpretation. Point clouds are processed to generate 33 new features related to the rock cliff differences, predominant differences, or orientation for classification with 11 machine learning models, combined with 2 undersampling and 13 oversampling methods. The proposed methodology is divided into two software packages: point cloud monitoring and cluster classification. The prediction model applied in two study cases in the Montserrat conglomeratic massif (Barcelona, Spain) reveal that a reduction of 98% in the initial number of clusters is sufficient to identify the totality of rockfalls in the first case study. The second case study requires a 96% reduction to identify 90% of the rockfalls, suggesting that the homogeneity of the rockfall characteristics is a key factor for the correct prediction of the machine learning models.

Seismic data can provide information to deduce the occurrence of mass movement events, their release time, event location and dynamics characterization [1]. Nevertheless, the effect of local site amplifications, the level of seismic noise and the frequency content of the signals are important constraints to correctly identify and describe these types of events. In this article we provide data on: site effects, power spectral densities, polarization particle motion and spectrograms generated by a rockslide (∼450 m3) (hereinafter NR) recorded in two permanent seismic stations (EPOB and POBL) located ∼10 km from the source. Original data are available through the International Federation of Digital Seismograph Networks (FDSN, http://www.fdsn.org) for POBL and on request from Instituto Geográfico Nacional (IGN, http://www.ign.es) for EPOB. POBL and EPOB site effects analysis by means of Horizontal-to-Vertical spectral ratio (H/V) technique shows important signatures in POBL signal between 1 and 10 Hz, indicating strong amplification effects at these frequencies, not present in EPOB. For frequencies >1 Hz, Power Spectral Densities (PSD) are higher in POBL than in EPOB, indicating that POBL is noisier than EPOB. Based on the H/V and PSD analyzes, the EPOB station data was deemed preferable over the POBL, to conduct the research presented in the related article [1]. Particle polarization motion data enabled the identification of the arrivals of P, S, and superficial waves, confirming that Pg waves were correctly identified, providing necessary information for the event location in the research article [1]. Moreover, EPOB and POBL spectrograms together with the Fourier transform are included to analyze their content in the frequency domain showing that the expected high frequency phenomenon of the rockslide recorded at 10 km is attenuated and only the low frequency content between 1 and 15 Hz is recorded. Keywords: Seismic data, Translational rock slide, Seismic quality control, Waves identification, Site effects

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).

Photogrammetric models have become a standard tool for the study of surfaces, structures and natural elements. As an alternative to Light Detection and Ranging (LiDAR), photogrammetry allows 3D point clouds to be obtained at a much lower cost. This paper presents an enhanced workflow for image-based 3D reconstruction of high-resolution models designed to work with fixed time-lapse camera systems, based on multi-epoch multi-images (MEMI) to exploit redundancy. This workflow is part of a fully automatic working setup that includes all steps: from capturing the images to obtaining clusters from change detection. The workflow is capable of obtaining photogrammetric models with a higher quality than the classic Structure from Motion (SfM) time-lapse photogrammetry workflow. The MEMI workflow reduced the error up to a factor of 2 when compared to the previous approach, allowing for M3C2 standard deviation of 1.5 cm. In terms of absolute accuracy, using LiDAR data as a reference, our proposed method is 20% more accurate than models obtained with the classic workflow. The automation of the method as well as the improvement of the quality of the 3D reconstructed models enables accurate 4D photogrammetric analysis in near-real time.

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.

On 8th February 1996, in the north-western part of Andorra in the Pyrenees, the Les Fonts d’Arinsal (LFd’A) pure powder avalanche was triggered, descending some 1200 m to the bottom of the Arinsal valley and continuing up the opposite slope for about 200 m. This size 4–5 avalanche reached velocities of up to 80 ms−1, devastated 18 ha of forest, involved a minimum volume of up to 1.8 × 106 m−3 and caused major damage to eight buildings. Fortunately, no one was injured thanks to an evacuation, but 322 people lost their properties. This study describes the physical characteristics of the LFd’A avalanche path and provides data on earlier avalanches, the meteorological synoptic situation and snowpack conditions that generated the avalanche episode, the warning and preventive actions carried out, the effects and evidence of the large avalanche, and the defence system implemented afterwards. A discussion of the avalanche dynamics based on observations and damage, including the role of snow entrainment, the total lack of characteristic dense flow deposits, as well as the evidence of a two-phase flow (fluidisation and suspension), is presented. This case study is an example of a paradigmatic large, pure powder, dry-snow avalanche, which will be useful for model calibration.

The sensitive mountain catchment of Portainé (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 o acquire a holistic understanding of the catchment’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 Portainé, but also in other nearby, similar basins.