Nepal's quake-driven landslide hazards Large earthquakes can trigger dangerous landslides across a wide geographic region. The 2015 M w 7.8 Gorhka earthquake near Kathmandu, Nepal, was no exception. Kargal et al. used remote observations to compile a massive catalog of triggered debris flows. The satellite-based observations came from a rapid response team assisting the disaster relief effort. Schwanghart et al. show that Kathmandu escaped the historically catastrophic landslides associated with earthquakes in 1100, 1255, and 1344 C.E. near Nepal's second largest city, Pokhara. These two studies underscore the importance of determining slope stability in mountainous, earthquake-prone regions. Science , this issue p. 10.1126/science.aac8353 ; see also p. 147

There is a gap between lab experiments where resistivity–soil moisture relations are generally very good and field studies in complex environmental settings where relations are always less good and complicated by many factors. An experiment was designed where environmental settings are more controlled, the best outside laboratory, to assess the transferability from lab to outdoor. A field experiment was carried out to evaluate the use of electric resistivity tomography (ERT) for monitoring soil moisture dynamics over a period of 67 days. A homogeneous site in the central part of The Netherlands was selected consisting of grass pasture on an aeolian sand soil profile. ERT values were correlated to gravimetric soil moisture samples for five depths at three different dates. Correlations ranged from 0.43 to 0.73 and were best for a soil depth of 90 cm. Resistivity patterns over time (time-lapse ERT) were analyzed and related to rainfall events where rainfall infiltration patterns could be identified. Duplicate ERT measurements showed that the noise level of the instrument and measurements is low and generally below 3% for the soil profile below the mixed layer but above the groundwater. Although the majority of the measured resistivity patterns could be well explained, some artefacts and dynamics were more difficult to clarify, even so in this homogeneous field situation. The presence of an oak tree with its root structure and a ditch with surface water with higher conductivity may have an impact on the resistivity pattern in the soil profile and over time. We conclude that ERT allows for detailed spatial measurement of local soil moisture dynamics resulting from precipitation although field experiments do not yield accuracies similar to laboratory experiments. ERT approaches are suitable for detailed spatial analyses where probe or sample-based methods are limited in reach or repeatability.

<p>Roads can both increase and decrease the likelihood of landslides occurring in a given region. This might be due to (i) mapping biases when compiling landslide inventories, (ii) the influence of the road on the landslide susceptibility. Here, we present a spatial statistical analysis of landslide proximity to roads across a range of geographic settings and landslide inventory types. We examine the proximity of landslide centroids to roads at regional to national scales using 12 diverse landslide inventories with variations in inventory type (6 triggered event, 6 multi-temporal), mapping method (1 field-based, 6 remote sensing, 5 a combination of mapping methods), and countries of origin distinguished by their human development index (HDI) (6 high and 6 low HDI). Each inventory contains 270 < n<sub>Landslides</sub> < 81,000 landslides with inventory regional extents ranging from 80 km<sup>2</sup> < A<sub>inventory</sub> < 385,000 km<sup>2</sup>. We have developed a PyQGIS tool that calculates the distance between each landslide centroid and the closest road vector within the same watershed. From these distance values, we create a density distribution of landslides as a function of distance from roads for that inventory. We then compare each inventory’s density distribution of landslide-to-road distance to a set of randomly generated points and their distances to roads. For the 12 inventories, we find that the landslide density near roads compared to random points is greater in 3 inventories, equal in 3 inventories, and lower in 6 inventories. We find that a comparison between landslides and random points describes each inventory well in terms of road density. We divide the 12 inventories into 4 typologies with different potential explanations for each group. We believe there is evidence of mapping bias towards roads for the typology with 3 inventories that have greater landslide density (compared to random points), which suggests that a more nuanced use of road proximity within landslide susceptibility models should be adopted. Further research should be done to understand the interactions between landslides and proximity to roads at the regional to national scale.</p>

<p>Landslide domains are a useful tool for characterising and subdividing a region into homogenous units reflecting the style of landsliding, which is controlled by the environmental characteristics (e.g. geology, relief). Landslide domains can provide a framework for the application of landslide knowledge obtained from a data-rich area across areas within the domain that are less data rich but have similar environmental characteristics. We have constructed landslide domains for East Sikkim using a landslide inventory, geology, relief and expert-based knowledge of landslide processes in the region. First, we catalogued landslide processes in East Sikkim using peer-reviewed literature, supplementing this with the mapping of over 450 translational landslides and debris flows in Google Earth through visual analysis utilising process knowledge from the catalogue. Several dozens of old landslides were mapped with stereographic analysis of four Cartosat-1 stereo pairs (90 km<sup>2</sup>) captured in March and December of 2011. Morphometric maps were constructed from Aster GDEM. Finally, the driving environmental characteristics for each process have been determined via statistical analyses to inform expert-driven construction of the landslide domains. We find that landslide domains explain landslide processes in East Sikkim well, but they may be limited by the amount of data that is available. The construction of landslide domains is flexible and can be applied to many different areas. Future work includes the testing of large-scale regions and inclusion into susceptibility models, where we hope that they will facilitate the construction of more accurate and representative susceptibility maps.</p>