Evolution of natural risk: research framework and perspectives

International audienceThis study presents a conceptual framework for addressing temporal variation in natural risk. Numerous former natural risk analyses and investigations have demonstrated that time and related changes have a crucial influence on risk. For natural hazards, time becomes a factor for a number of reasons. Using the example of landslides to illustrate this point, it is shown that: 1. landslide history is important in determining probability of occurrence, 2. the significance of catchment variables in explaining landslide susceptibility is dependent on the time scale chosen, 3. the observer's perception of the geosystem's state changes with different time spans, and 4. the system's sensitivity varies with time. Natural hazards are not isolated events but complex features that are connected with the social system. Similarly, elements at risk and their vulnerability are highly dynamic through time, an aspect that is not sufficiently acknowledged in research. Since natural risk is an amalgam of hazard and vulnerability, its temporal behaviour has to be considered as well. Identifying these changes and their underlying processes contributes to a better understanding of natural risk today and in the future. However, no dynamic models for natural risks are currently available. Dynamic behaviour of factors affecting risk is likely to create increasing connectivity and complexity. This demands a broad approach to natural risk, since the concept of risk encapsulates aspects of many disciplines and has suffered from single-discipline approaches in the past. In New Zealand, dramatic environmental and social change has occurred in a relatively short period of time, graphically demonstrating the temporal variability of the geosystem and the social system. To understand these changes and subsequent interactions between both systems, a holistic perspective is needed. This contribution reviews available frameworks, demonstrates the need for further concepts, and gives research perspectives on a New Zealand example

Rapid and Efficient Arsenic Removal by Iron Electrocoagulation Enabled with in Situ Generation of Hydrogen Peroxide.

Millions of people are exposed to toxic levels of dissolved arsenic in groundwater used for drinking. Iron electrocoagulation (FeEC) has been demonstrated as an effective technology to remove arsenic at an affordable price. However, FeEC requires long operating times (∼hours) to remove dissolved arsenic due to inherent kinetics limitations. Air cathode Assisted Iron Electrocoagulation (ACAIE) overcomes this limitation by cathodically generating H2O2 in situ. In ACAIE operation, rapid oxidation of Fe(II) and complete oxidation and removal of As(III) are achieved. We compare FeEC and ACAIE for removing As(III) from an initial concentration of 1464 μg/L, aiming for a final concentration of less than 4 μg/L. We demonstrate that at short electrolysis times (0.5 min), i.e., high charge dosage rates (1200 C/L/min), ACAIE consistently outperformed FeEC in bringing arsenic levels to less than WHO-MCL of 10 μg/L. Using XRD and XAS data, we conclusively show that poor arsenic removal in FeEC arises from incomplete As(III) oxidation, ineffective Fe(II) oxidation and the formation of Fe(II-III) (hydr)oxides at short electrolysis times (<20 min). Finally, we report successful ACAIE performance (retention time 19 s) in removing dissolved arsenic from contaminated groundwater in rural California

Collision induced spatial organization of microtubules

The dynamic behavior of microtubules in solution can be strongly modified by interactions with walls or other structures. We examine here a microtubule growth model where the increase in size of the plus-end is perturbed by collisions with other microtubules. We show that such a simple mechanism of constrained growth can induce ordered structures and patterns from an initially isotropic and homogeneous suspension. First, microtubules self-organize locally in randomly oriented domains that grow and compete with each other. By imposing even a weak orientation bias, external forces like gravity or cellular boundaries may bias the domain distribution eventually leading to a macroscopic sample orientation.Comment: Submitted to Biophysical Journa

A hybrid microfluidic platform for cell-based assays via diffusive and convective trans-membrane perfusion

We present a novel 3D hybrid assembly of a polymer microfluidic chip with polycarbonate track-etched membrane (PCTEM) enabling membrane-supported cell culture. Two chip designs have been developed to establish either diffusive or convective reagent delivery using the integrated PCTEM. While it is well suited to a range of cell-based assays, we specifically employ this platform for the screening of a common antitumor chemotoxic agent (mitomycin C – MMC) on the HL60 myeloid leukemia cell line. The toxic activity of MMC is based on the generation of severe DNA damage in the cells. Using either mode of operation, the HL60 cells were cultured on-chip before, during, and after exposure to MMC at concentrations ranging from 0 to 50 lM. Cell viability was analysed off-chip by the trypan blue dye exclusion assay. The results of the on-chip viability assay were found to be consistent with those obtained off-chip and indicated ca. 40% cell survival at MMC concentration of 50 lM. The catalogue of capabilities of the here described cell assay platform comprises of (i) the culturing of cells either under shear-free conditions or under induced through-membrane flows, (ii) the tight time control of the reagent exposure, (iii) the straightforward assembly of devices, (iv) the flexibility on the choice of the membrane, and, prospectively, (v) the amenability for large-scale parallelization

Developing a spatiotemporal model to integrate landslide susceptibility and critical rainfall conditions. A practical model applied to Rio de Janeiro municipality

Despite being a landscape evolution element, landslides pose a significant threat to infrastructure, property, and human life around the globe. In Brazil, this has been a major source of concern for many years. Over the last decades, especially in the humid areas of Brazil, landslide occurrences have become more frequent and catastrophic (Pelech et al., 2019). Especially in large and medium-sized cities, poorly-regulated living conditions and a progressing global warming scenario will likely increase the frequency, magnitude, and possibly damagecaused by landslides (Marengo et al., 2021). On the other hand, despite the efforts of local authorities to forecast and mitigate the phenomena, not enough is currently being done in terms of preparedness for future events, especially concerning research (Dias et al., 2021).Due to the geomorphological and climatic settings, the municipality of Rio de Janeiro (~1,200 km²) is often affected by landslides (Coelho Netto et al., 2007; 2009). According to the Brazilian Institute of Geography and Statistics (IBGE, 2021), the municipality has 6.7 million inhabitants, of which circa 20-25% lives in the favelas. These communities, usually located on hill slopes, face diverse challenges such as poor basic infrastructure, lack of sanitation systems, and high criminality, which tend to diminish the inhabitants’ awareness of potential landslide hazards. On the other hand, the municipality of Rio de Janeiro has systematically tracked rainfall data for the last decades. Such data comprises 33 stations, recording measurements every 15 minutes. Rainfall data is availablefor a few decades and comprise 33 stations recording measurements every 15 minutes. Also, the availability of high-resolution DTM and DEM (obtained through LiDAR with a 15 cm resolution), orthoimagery updated quasiyearly, and a suitable landslide inventory, turns Rio de Janeiro into a promising real-life laboratory for suggesting and enhancing modeling solutions that may provide valuable tools for landslide emergency preparedness, management, and response.Building upon the findings of Steger et al, 2022, the present research represents a joint effort to suggest a methodological framework to develop a dynamic landslide model that integrates static predisposing factors with dynamic rainfall conditions. Data-driven methods (e.g., Generalized Additive Models) will be used to establish statistical relationships between the static factors, the dynamic rainfall conditions prior to a potential landslide, and the landslide occurrence in space and time. The outcomes may be used by stakeholders to strategically prepare for potential rainfall events leading to landslides and possibly to improve early warning systems. Data collection and preparation are currently happening, and the analysis will follow. Partial results will be presented at the 6th World Landslide Forum

Froude number scaling unifies impact trajectories into granular media across gravitational conditions

The interactions of solid objects with granular media is countered by a resistance force that stems from frictional forces between the grains and the media's resistance to inertia imposed by the intruder. Earlier theories of granular intrusion have suggested an additive contribution of these two families of forces and had tremendous success in predicting resistive forces on arbitrary shaped objects. However, it remains unclear how these forces are influenced by gravitational conditions. We examine the role of gravity on surface impact behavior into cohesionless granular media using hundreds of soft-sphere discrete element simulations, we demonstrate that the outcome of impacts remain qualitatively similar under varying gravitational conditions if initial velocities are scaled with the Froude number, suggesting an underlying law. Using theoretical arguments, we provide reasoning for the observed universality and show that there is a hidden dependency in resistive forces into granular media on Froude number. Following the theoretical framework, we show that Froude number scaling precisely collapses impact trajectories across gravitational conditions, setting the foundation for explorations in granular behavior beyond Earth

Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution

Models of landscape evolution provide insight into the geomorphic history of specific field areas, create testable predictions of landform development, demonstrate the consequences of current geomorphic process theory, and spark imagination through hypothetical scenarios. While the last 4 decades have brought the proliferation of many alternative formulations for the redistribution of mass by Earth surface processes, relatively few studies have systematically compared and tested these alternative equations. We present a new Python package, terrainbento 1.0, that enables multi-model comparison, sensitivity analysis, and calibration of Earth surface process models. Terrainbento provides a set of 28 model programs that implement alternative transport laws related to four process elements: hillslope processes, surface-water hydrology, erosion by flowing water, and material properties. The 28 model programs are a systematic subset of the 2048 possible numerical models associated with 11 binary choices. Each binary choice is related to one of these four elements – for example, the use of linear or nonlinear hillslope diffusion. Terrainbento is an extensible framework: base classes that treat the elements common to all numerical models (such as input/output and boundary conditions) make it possible to create a new numerical model without reinventing these common methods. Terrainbento is built on top of the Landlab framework such that new Landlab components directly support the creation of new terrainbento model programs. Terrainbento is fully documented, has 100&thinsp;% unit test coverage including numerical comparison with analytical solutions for process models, and continuous integration testing. We support future users and developers with introductory Jupyter notebooks and a template for creating new terrainbento model programs. In this paper, we describe the package structure, process theory, and software implementation of terrainbento. Finally, we illustrate the utility of terrainbento with a benchmark example highlighting the differences in steady-state topography between five different numerical models.</p

Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

Landslide forecasting and early warning has a long tradition in landslide research and is primarily carried out based on empirical and statistical approaches, e.g., landslide-triggering rainfall thresholds. In the last decade, flood forecasting started the operational mode of so-called ensemble prediction systems following the success of the use of ensembles for weather forecasting. These probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty when larger areas are considered and explicitly introduce them into the model results. Now that highly detailed numerical weather predictions and high-performance computing are becoming more common, physically based landslide forecasting for larger areas is becoming feasible, and the landslide research community could benefit from the experiences that have been reported from flood forecasting using ensemble predictions. This paper reviews and summarizes concepts of ensemble prediction in hydrology and discusses how these could facilitate improved landslide forecasting. In addition, a prototype landslide forecasting system utilizing the physically based TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model is presented to highlight how such forecasting systems could be implemented. The paper concludes with a discussion of challenges related to parameter variability and uncertainty, calibration and validation, and computational concerns.</p

Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution

Models of landscape evolution provide insight into the geomorphic history of specific field areas, create testable predictions of landform development, demonstrate the consequences of current geomorphic process theory, and spark imagination through hypothetical scenarios. While the last 4 decades have brought the proliferation of many alternative formulations for the redistribution of mass by Earth surface processes, relatively few studies have systematically compared and tested these alternative equations. We present a new Python package, terrainbento 1.0, that enables multi-model comparison, sensitivity analysis, and calibration of Earth surface process models. Terrainbento provides a set of 28 model programs that implement alternative transport laws related to four process elements: hillslope processes, surface-water hydrology, erosion by flowing water, and material properties. The 28 model programs are a systematic subset of the 2048 possible numerical models associated with 11 binary choices. Each binary choice is related to one of these four elements &ndash; for example, the use of linear or nonlinear hillslope diffusion. Terrainbento is an extensible framework: base classes that treat the elements common to all numerical models (such as input/output and boundary conditions) make it possible to create a new numerical model without reinventing these common methods. Terrainbento is built on top of the Landlab framework such that new Landlab components directly support the creation of new terrainbento model programs. Terrainbento is fully documented, has 100 % unit test coverage including numerical comparison with analytical solutions for process models, and continuous integration testing. We support future users and developers with introductory Jupyter notebooks and a template for creating new terrainbento model programs. In this paper, we describe the package structure, process theory, and software implementation of terrainbento. Finally, we illustrate the utility of terrainbento with a benchmark example highlighting the differences in steady-state topography between five different numerical models.</p