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Process-based rockfall simulation models attempt to better emulate rockfall dynamics to different degrees. As no model is perfect, their development is often accompanied and validated by the valuable collection of rockfall databases covering a range of site geometries, rock masses, velocities, and related energies that the models are designed for. Additionally, such rockfall data can serve as a base for assessing the model’s sensitivity to different parameters, evaluating their predictability and helping calibrate the model’s parameters from back calculation and analyses. As the involved rock volumes/masses increase, the complexity of conducting field-test experiments to build up rockfall databases increases to a point where such experiments become impracticable. To the author’s knowledge, none have reconstructed rockfall data in 3D from real events involving block fragments of approximately 500 metric tons. A back analysis of the 2015 Mel de la Niva rockfall event is performed in this paper, contributing to a novel documentation in terms of kinetic energy values, bounce heights, velocities, and 3D lateral deviations of these rare events involving block fragments of approximately 200 m3. Rockfall simulations are then performed on a “per-impact” basis to illustrate how the reconstructed data from the site can be used to validate results from simulation models.

The spreading use of remote techniques is in our daily life benefits to ease and/or speed up the acquisition and analysis of geographical data that can be meaningful for risk assessment or for taking decisions for prevention measures.Here is presented one of the possible applications for the Unmanned Aerial Vehicle (UAV) acquisition, to evaluate the volume of eroded soils in a crop field due to washout after heavy rains. The case study is located North of Lausanne (Switzerland), in the village of Savigny. It is a crop field with a gentle slope where we can clearly see washout gullies appearing after rainfalls. A great number of small water streams disappeared for more intense agriculture which is the case here : According to topographical maps, a small stream was flowing in the past but disappeared after 2004. It is interesting to see that after important rainfalls, gullies appear that could correspond to old small stream patterns. The data acquisition survey of October 30th, 2020 was done by means of a DJI Phantom 4 RTK flying at an altitude of about 20m and the Pix4d Capture planning mission application. To process the obtained 800 images, the new Pix4D Matic software was tested to get a fast dense point cloud with GSD ~1 cm accuracy, a DEM and an orthophoto. The dense point cloud was then analyzed with two compared methods to estimate the washout volumes, which are (1) inverse Sloping Local Base Level; and (2) Point cloud segmentation based on normal vectors and curvatures.As a result, these two methods gave a first estimation of the eroded volume of around 15m3 over a surface of 9 hectares which corresponds to an erosion rate of 1,7m3/hectare. These remote and non-destructive techniques are fast and easy compared to conventional field surveys, and the data acquisition and processing could be automated. In conclusion, these techniques provide a relatively low-cost time-series datasets processing to monitor and quantify the ongoing gully erosion. Further investigation would be to keep recording the volume and erosion rate estimations after important rainfalls, when clear new gullies appear and to record in the meanwhile the rainfall intensity. This could help assess in a second step the relationship between the erosion rate and the rainfall intensity and control if this relation follows a power-law function. Such a study could also give some clues about the possible impact of climate changes on erosions in crop fields.

Analog methods (AMs) allow predicting local meteorological variables of interest (predictand), such as the daily precipitation, based on synoptic variables (predictors). They rely on the hypothesis that similar atmospheric conditions are likely to result in similar local effects. The statistical relationship is first defined (e.g. which predictors, and how many subsampling steps) and calibrated (e.g. which spatial domain, and how many analogues) before being applied to the target period, may it be for operational forecasting or for climate impact studies. A benefit of AMs is that they are lightweight and can provide valuable results for a negligible cost. AtmoSwing is an open source software that implements different AM variants in a very flexible way, so that they can be easily configured by means of XML files. It is written in C++, is object-oriented and multi-platform. AtmoSwing provides four tools: the Optimizer to establish the relationship between the predictand and predictors, the Downscaler to apply the method for climate impact studies, the Forecaster to perform operational forecasts, and the Viewer to display the results. The Optimizer provides a semi-automatic sequential approach, as well as Monte-Carlo analyses, and a global optimization technique by means of Genetic Algorithms. It calibrates the statistical relationship that can be later applied in a forecasting or climatic context. The Downscaler takes as input the outputs of climate models, either GCMs or RCMs in order to provide a downscaled time series of the predictand of interest at a local scale. The Forecaster automatically downloads and reads operational NWP outputs to provide operational forecasting of the predictand of interest. The processing of a forecast is extremely lightweight in terms of computing resources; it can indeed run on almost any computer. The Viewer displays the forecasts in an interactive GIS environment. It contains several layers of syntheses and details in order to provide a quick overview of the potential critical situations in the coming days, as well as the possibility for the user to go into the details of the forecasted predictand distribution.

Analogue methods (AMs) rely on the hypothesis that similar situations, in terms of atmospheric circulation, are likely to result in similar local or regional weather conditions. These methods consist of sampling a certain number of past situations, based on different synoptic-scale meteorological variables (predictors), in order to construct a probabilistic prediction for a local weather variable of interest (predictand). They are often used for daily precipitation prediction, either in the context of real-time forecasting, reconstruction of past weather conditions, or future climate impact studies. The relationship between predictors and predictands is defined by several parameters (predictor variable, spatial and temporal windows used for the comparison, analogy criteria, and number of analogues), which are often calibrated by means of a semi-automatic sequential procedure that has strong limitations. AMs may include several subsampling levels (e.g. first sorting a set of analogs in terms of circulation, then restricting to those with similar moisture status). The parameter space of the AMs can be very complex, with substantial co-dependencies between the parameters. Thus, global optimization techniques are likely to be necessary for calibrating most AM variants, as they can optimize all parameters of all analogy levels simultaneously. Genetic algorithms (GAs) were found to be successful in finding optimal values of AM parameters. They allow taking into account parameters inter-dependencies, and selecting objectively some parameters that were manually selected beforehand (such as the pressure levels and the temporal windows of the predictor variables), and thus obviate the need of assessing a high number of combinations. The performance scores of the optimized methods increased compared to reference methods, and this even to a greater extent for days with high precipitation totals. The resulting parameters were found to be relevant and spatially coherent. Moreover, they were obtained automatically and objectively, which reduces efforts invested in exploration attempts when adapting the method to a new region or for a new predictand. In addition, the approach allowed for new degrees of freedom, such as a weighting between the pressure levels, and non overlapping spatial windows. Genetic algorithms were then used further in order to automatically select predictor variables and analogy criteria. This resulted in interesting outputs, providing new predictor-criterion combinations. However, some limitations of the approach were encountered, and the need of the expert input is likely to remain necessary. Nevertheless, letting GAs exploring a dataset for the best predictor for a predictand of interest is certainly a useful tool, particularly when applied for a new predictand or a new region under different climatic characteristics.

Analogue methods (AMs) predict local weather variables (predictands), such as precipitation, by means of a statistical relationship with predictors at a synoptic scale. Predictors are extracted from reanalysis datasets that often have a six hourly time step. For precipitation forecasts, the predictand often consists of daily precipitation (06h to 30h UTC), given the length of their available archives, and the unavailability of equivalent archives at a finer time step. The optimal predictors to explain these daily precipitations have been obtained in a calibration procedure with fixed times of observation (e.g. geopotential heigths Z1000 at 12h UTC and Z500 at 24h UTC). In operational forecast, a new target situation is defined by its geopotential predictors at these fixed hours, i.e. Z1000 at 12h UTC and Z500 at 24h UTC. Usually, the search for candidate situations for this given target day is usually undertaken by comparing the state of the atmosphere at the same fixed hours of the day for both the target day and the candidate analogues. However, it can be expected that the best analogy among the past synoptic situations does not occur systematically at the same time of the day and that better candidates can be found by shifting to a different hour. With this assumption, a moving time window (MTW) was introduced to allow the search for candidates at different hours of the day (e.g. Z1000 at 00, 06, 12, 18 h UTC and Z500 at 12, 18, 24, 30 h UTC respectively). This MTW technique can only result in a better analogy in terms of the atmospheric circulation (compared to the method with fixed hours), with improved values of the analogy criterion on the entire distribution of analogue dates. A seasonal effect has also been identified, with larger improvements in winter than in summer. However, its interest in precipitation forecast can only be evaluated with an archive of the corresponding 24h-totals, i.e. not only 6-30h UTC totals, but also 0-24h, 12-12h and 18-18h totals). This was possible to assess on a set of stations from the Swiss hourly measurement network with rather long time-series. The prediction skill was found to have improved by the MTW, and even to a greater extent after recalibrating the AM parameters. Moreover, the improvement was greater for days with heavy precipitation, which are generally related to more dynamic atmospheric situations where timing is more specific. The use of the MTW in the AM can be considered for several applications in different contexts, may it be for operational forecasting or climate-related studies.

This manuscript presents a review on the application of a remote sensing technique (terrestrial laser scanning TLS) to a well known topic (rock slope characterization and monitoring). Although the number of publications on the use of TLS in rock slope studies has rapidly increased in the last 5 10years little effort has been made to review the key developments establish a code of best practice and unify future research approaches. The acquisition of dense 3D terrain information with high accuracy high data acquisition speed and increasingly efficient post processing workflows is helping to better quantify key parameters of rock slope instabilities across spatial and temporal scales ranging from cubic decimetres to millions of cubic metres and from hours to years respectively. Key insights into the use of TLS in rock slope investigations include: (a) the capability of remotely obtaining the orientation of slope discontinuities which constitutes a great step forward in rock mechanics; (b) the possibility to monitor rock slopes which allows not only the accurate quantification of rockfall rates across wide areas but also the spatio temporal modelling of rock slope deformation with an unprecedented level of detail. Studying rock slopes using TLS presents a series of key challenges from accounting for the fractal character of rock surface to detecting the precursory deformation that may help in the future prediction of rock failures. Further investigation on the development of new algorithms for point cloud filtering segmentation feature extraction deformation tracking and change detection will significantly improve our understanding on how rock slopes behave and evolve. Perspectives include the use of new 3D sensing devices and the adaptation of techniques and methods recently developed in other disciplines as robotics and 3D computer vision to rock slope instabilities research. © 2013 John Wiley Sons Ltd.

Economic estimation of the impact of rural earthen road construction in Phewa watershed. (DOCX 20Â kb)

The Analogue Method (AM) aims at forecasting a local meteorological variable of interest (the predictand), often the daily precipitation total, on the basis of a statistical relationship with synoptic predictor variables. A certain number of similar situations are sampled in order to establish the empirical conditional distribution which is considered as the prediction for a given date. The method is used in operational medium-range forecasting in several hydropower companies or flood forecasting services, as well as in climate impact studies. The statistical relationship is usually established by means of a semi-automatic sequential procedure that has strong limitations: it is made of successive steps and thus cannot handle parameters dependencies, and it cannot automatically optimize certain parameters, such as the selection of the pressure levels and the temporal windows on which the predictors are compared. A global optimization technique based on Genetic Algorithms was introduced in order to surpass these limitations and to provide a fully automatic and objective determination of the AM parameters. The parameters that were previously assessed manually, such as the selection of the pressure levels and the temporal windows, on which the predictors are compared, are now automatically determined. The next question is: Are Genetic Algorithms able to select the meteorological variable, in a reanalysis dataset, that is the best predictor for the considered predictand, along with the analogy criteria itself? Even though we may not find better predictors for precipitation prediction that the ones often used in Europe, due to numerous other studies which consisted in systematic assessments, the ability of an automatic selection offers new perspectives in order to adapt the AM for new predictands or new regions under different meteorological influences.

To explore connections between rock strength and rock falls, we undertook a comprehensive rock mechanics testing program for six granitic rock types in Yosemite Valley (California, USA) where rock falls are a common geomorphic and sometimes hazardous process. We collected samples from boulders located at the base of cliffs, with the inherent assumption that the intact boulders should provide reasonable estimates of full-strength values. Our testing program included unconfined compressive strength tests, triaxial compressive strength tests, Brazilian tensile strength tests, and Mode I fracture toughness strength testing using two different types of samples – chevron bend (CB) and cracked chevron notched Brazilian disk (CCNBD). Our results, consisting of 88 individual tests, provide the most detailed evaluation of rock strength in Yosemite Valley to date. These results provide the data needed to evaluate the various failure modes (e.g., shear failure of wedge instabilities, tensile failure of overhangs) that might be expected for rock falls from cliffs in Yosemite. We expect that these data will provide an important resource for the evaluation of rock falls and other geomorphological studies in Yosemite National Park.