Under the 2005 U.S. EPA Guidelines for Carcinogen Risk Assessment (1), evaluations of carcinogens rely on mode of action data to better inform dose response assessments. A reassessment of carbon tetrachloride, a model hepatotoxicant and carcinogen, provides an opportunity to incorporate into the assessment biologically relevant mode of action data on its carcinogenesis. Mechanistic studies provide evidence that metabolism of carbon tetrachloride via CYP2E1 to highly reactive free radical metabolites plays a critical role in the postulated mode of action. The primary metabolites, trichloromethyl and trichloromethyl peroxy free radicals, are highly reactive and are capable of covalently binding locally to cellular macromolecules, with preference for fatty acids from membrane phospholipids. The free radicals initiate lipid peroxidation by attacking polyunsaturated fatty acids in membranes, setting off a free radical chain reaction sequence. Lipid peroxidation is known to cause membrane disruption, resulting in the loss of membrane integrity and leakage of microsomal enzymes. By-products of lipid peroxidation include reactive aldehydes that can form protein and DNA adducts and may contribute to hepatotoxicity and carcinogenicity, respectively. Natural antioxidants, including glutathione, are capable of quenching the lipid peroxidation reaction. When glutathione and other antioxidants are depleted, however, opportunities for lipid peroxidation are enhanced. Weakened cellular membranes allow sufficient leakage of calcium into the cytosol to disrupt intracellular calcium homeostasis. High calcium levels in the cytosol activate calcium-dependent proteases and phospholipases that further increase the breakdown of the membranes. Similarly, the increase in intracellular calcium can activate endonucleases that can cause chromosomal damage and also contribute to cell death. Sustained cell regeneration and proliferation following cell death may increase the likelihood of unrepaired spontaneous, lipid peroxidation- or endonuclease-derived mutations that can lead to cancer. Based on this body of scientific evidence, doses that do not cause sustained cytotoxicity and regenerative cell proliferation would subsequently be protective of liver tumors if this is the primary mode of action. To fulfill the mode of action framework, additional research may be necessary to determine alternative mode(s) of action for liver tumors formed via carbon tetrachloride exposure.

Earthquakes impart an impressive force on epicentral landscapes, with immediate catastrophic hillslope response. However, their legacy on geomorphic process rates remains poorly constrained. We have determined the evolution of landslide rates in the epicentral areas of four intermediate to large earthquakes (M w , 6.6-7.6). In each area, landsliding correlates with the cumulative precipitation during a given interval. Normalizing for this meteorological forcing, landslide rates have been found to peak after an earthquake and decay to background values in 1-4 yr, with the decay time scale probably proportional to the earthquake magnitude. The transient pulse of landsliding is not related to external forcing such as rainfall or aftershocks, and we tentatively attribute it to the reduction and subsequent recovery of ground strength. Observed geomorphic trends are not linked with groundwater level changes or root system damage, both of which could affect substrate strength. We propose that they are caused by reversible damage of rock mass and/or loosening of regolith. Qualitative accounts of ground cracking due to strong ground motion abound, and our observations are circumstantial evidence of its potential importance in setting landscape sensitivity to meteorological forcing after large earthquakes.

We present a new, seismologically consistent expression for the total area and volume of populations of earthquake-triggered landslides. This model builds on a set of scaling relationships between key parameters, such as landslide spatial density, seismic ground acceleration, fault length, earthquake source depth, and seismic moment. To assess the model we have assembled and normalized a catalog of landslide inventories for 40 shallow, continental earthquakes. Low landscape steepness causes systematic overprediction of the total area and volume of landslides. When this effect is accounted for, the model predicts the total landslide volume of 63% of 40 cases to within a factor 2 of the volume estimated from observations (R 2 = 0.76). The prediction of total landslide area is also sensitive to the landscape steepness, but less so than the total volume, and it appears to be sensitive to controls on the landslide size-frequency distribution, and possibly the shaking duration. Some outliers are likely associated with exceptionally strong rock mass in the epicentral area, while others may be related to seismic source complexities ignored by the model. However, the close match between prediction and estimate for about two thirds of cases in our database suggests that rock mass strength is similar in many cases and that our simple seismic model is often adequate, despite the variety of lithologies and tectonic settings covered. This makes our expression suitable for integration into landscape evolution models and application to the anticipation or rapid assessment of secondary hazards associated with earthquakes.

Earthquake‐induced landslide (EQIL) inventories are essential tools to extend our knowledge of the relationship between earthquakes and the landslides they can trigger. Regrettably, such inventories are difficult to generate and therefore scarce, and the available ones differ in terms of their quality and level of completeness. Moreover, access to existing EQIL inventories is currently difficult because there is no centralized database. To address these issues, we compiled EQIL inventories from around the globe based on an extensive literature study. The database contains information on 363 landslide‐triggering earthquakes and includes 66 digital landslide inventories. To make these data openly available, we created a repository to host the digital inventories that we have permission to redistribute through the U.S. Geological Survey ScienceBase platform. It can grow over time as more authors contribute their inventories. We analyze the distribution of EQIL events by time period and location, more specifically breaking down the distribution by continent, country, and mountain region. Additionally, we analyze frequency distributions of EQIL characteristics, such as the approximate area affected by landslides, total number of landslides, maximum distance from fault rupture zone, and distance from epicenter when the fault plane location is unknown. For the available digital EQIL inventories, we examine the underlying characteristics of landslide size, topographic slope, roughness, local relief, distance to streams, peak ground acceleration, peak ground velocity, and Modified Mercalli Intensity. Also, we present an evaluation system to help users assess the suitability of the available inventories for different types of EQIL studies and model development.

We provide a database of the coseismic geological surface effects following the Mw 6.5 Norcia earthquake that hit central Italy on 30 October 2016. This was one of the strongest seismic events to occur in Europe in the past thirty years, causing complex surface ruptures over an area of >400 km2. The database originated from the collaboration of several European teams (Open EMERGEO Working Group; about 130 researchers) coordinated by the Istituto Nazionale di Geofisica e Vulcanologia. The observations were collected by performing detailed field surveys in the epicentral region in order to describe the geometry and kinematics of surface faulting, and subsequently of landslides and other secondary coseismic effects. The resulting database consists of homogeneous georeferenced records identifying 7323 observation points, each of which contains 18 numeric and string fields of relevant information. This database will impact future earthquake studies focused on modelling of the seismic processes in active extensional settings, updating probabilistic estimates of slip distribution, and assessing the hazard of surface faulting.

Abstract. Rainfall-induced landslides are a common and significant source of damages and fatalities worldwide. Still, we have little understanding of the quantity and properties of landsliding that can be expected for a given storm and a given landscape, mostly because we have few inventories of rainfall-induced landslides caused by single storms. Here we present six new comprehensive landslide event inventories coincident with well identified rainfall events. Combining these datasets, with two previously published datasets, we study their statistical properties and their relations to topographic slope distribution and storm properties. Landslide metrics (such as total landsliding, peak landslide density, or landslide distribution area) vary across 2 to 3 orders of magnitude but strongly correlate with the storm total rainfall, varying over almost 2 orders of magnitude for these events. Applying a normalization on the landslide run-out distances increases these correlations and also reveals a positive influence of total rainfall on the proportion of large landslides. The nonlinear scaling of landslide density with total rainfall should be further constrained with additional cases and incorporation of landscape properties such as regolith depth, typical strength or permeability estimates. We also observe that rainfall-induced landslides do not occur preferentially on the steepest slopes of the landscape, contrary to observations from earthquake-induced landslides. This may be due to the preferential failures of larger drainage area patches with intermediate slopes or due to the lower pore-water pressure accumulation in fast-draining steep slopes. The database could be used for further comparison with spatially resolved rainfall estimates and with empirical or mechanistic landslide event modeling.

Abstract. In active mountain belts with steep terrain, bedrock landsliding is a major erosional agent. In the Himalayas, landsliding is driven by annual hydro-meteorological forcing due to the summer monsoon and by rarer, exceptional events, such as earthquakes. Independent methods yield erosion rate estimates that appear to increase with sampling time, suggesting that rare, high-magnitude erosion events dominate the erosional budget. Nevertheless, until now, neither the contribution of monsoon and earthquakes to landslide erosion nor the proportion of erosion due to rare, giant landslides have been quantified in the Himalayas. We address these challenges by combining and analysing earthquake- and monsoon-induced landslide inventories across different timescales. With time series of 5 m satellite images over four main valleys in central Nepal, we comprehensively mapped landslides caused by the monsoon from 2010 to 2018. We found no clear correlation between monsoon properties and landsliding and a similar mean landsliding rate for all valleys, except in 2015, where the valleys affected by the earthquake featured ∼5–8 times more landsliding than the pre-earthquake mean rate. The long-term size–frequency distribution of monsoon-induced landsliding (MIL) was derived from these inventories and from an inventory of landslides larger than ∼0.1 km2 that occurred between 1972 and 2014. Using a published landslide inventory for the Gorkha 2015 earthquake, we derive the size–frequency distribution for earthquake-induced landsliding (EQIL). These two distributions are dominated by infrequent, large and giant landslides but under-predict an estimated Holocene frequency of giant landslides (> 1 km3) which we derived from a literature compilation. This discrepancy can be resolved when modelling the effect of a full distribution of earthquakes of variable magnitude and when considering that a shallower earthquake may cause larger landslides. In this case, EQIL and MIL contribute about equally to a total long-term erosion of ∼2±0.75 mm yr−1 in agreement with most thermo-chronological data. Independently of the specific total and relative erosion rates, the heavy-tailed size–frequency distribution from MIL and EQIL and the very large maximal landslide size in the Himalayas indicate that mean landslide erosion rates increase with sampling time, as has been observed for independent erosion estimates. Further, we find that the sampling timescale required to adequately capture the frequency of the largest landslides, which is necessary for deriving long-term mean erosion rates, is often much longer than the averaging time of cosmogenic 10Be methods. This observation presents a strong caveat when interpreting spatial or temporal variability in erosion rates from this method. Thus, in areas where a very large, rare landslide contributes heavily to long-term erosion (as the Himalayas), we recommend 10Be sample in catchments with source areas > 10 000 km2 to reduce the method mean bias to below ∼20 % of the long-term erosion.

Abstract. Inventories of individually delineated landslides are a key to understanding landslide physics and mitigating their impact. They permit assessment of area–frequency distributions and landslide volumes, and testing of statistical correlations between landslides and physical parameters such as topographic gradient or seismic strong motion. Amalgamation, i.e. the mapping of several adjacent landslides as a single polygon, can lead to potentially severe distortion of the statistics of these inventories. This problem can be especially severe in data sets produced by automated mapping. We present five inventories of earthquake-induced landslides mapped with different materials and techniques and affected by varying degrees of amalgamation. Errors on the total landslide volume and power-law exponent of the area–frequency distribution, resulting from amalgamation, may be up to 200 and 50%, respectively. We present an algorithm based on image and digital elevation model (DEM) analysis, for automatic identification of amalgamated polygons. On a set of about 2000 polygons larger than 1000 m2, tracing landslides triggered by the 1994 Northridge earthquake, the algorithm performs well, with only 2.7–3.6% incorrectly amalgamated landslides missed and 3.9–4.8% correct polygons incorrectly identified as amalgams. This algorithm can be used broadly to check landslide inventories and allow faster correction by automating the identification of amalgamation.