Ashok Dahal

• Master of Science
• PhD at University of Twente

Assessing landslide risk is a fundamental requirement to plan suitable prevention actions. To date, most risk studies focus on individual slopes or catchments. Whereas regional, national or continental scale assessments are hardly available because of methodological and/or data limitations. In this contribution, we present an overview of all requir...

Spatiotemporal patterns of earth surface deformation are influenced by a combination of static and dynamic environmental characteristics specific to any landscape of interest. Nowadays, these patterns can be captured for larger areas using Inter-ferometric Synthetic-Aperture Radar (InSAR) technologies and yet, their spatial prediction has been poor...

Scientific advancements often emerge from pivotal discoveries and technological breakthroughs, expanding the frontiers of exploration. In geoscience, natural hazard studies have predominantly focused on terrestrial environments, while submarine settings remain relatively unexplored due to the scarcity of high-resolution data, particularly in deep-s...

Topographic amplification is caused by the interaction between seismic waves and rough terrains. It increases shaking levels on hilltops and could lead stable slopes to the brink of failure. However, its contribution to coseismic landslide occurrence is yet to be quantified over landscapes shaken by strong earthquakes. Here, we examine how topograp...

The most adopted definition of landslide hazard combines spatial information about landslide location (susceptibility), threat (intensity), and frequency (return period). Only the first two elements are usually considered and estimated when working over vast areas. Even then, separate models constitute the standard, with frequency being rarely inve...

There is an urgent need for accurate and effective Landslide Early Warning Systems (LEWS). Most LEWS are currently based on temporally-aggregated measures of rainfall derived from either in-situ measurements or satellite-based rainfall estimates. Relying on a summary metric of precipitation may not capture the complexity of the rainfall signal and...

For decades, solutions to regional scale landslide prediction have mostly relied on data-driven models, by definition, disconnected from the physics of the failure mechanism. The success and spread of such tools came from the ability to exploit proxy variables rather than explicit geotechnical ones, as the latter are prohibitive to acquire over bro...

Regional debris-flow hazard assessments provide consistent information on potential hazards over large areas, often with limited available data. Different approaches to regional debris-flow hazard assessment include heuristic, empirical, statistical, or physically-based techniques. The resulting product is often a debris-flow susceptibility map tha...

Until now, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physically based models. The part of the geoscientific community in developing data-driven models has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimate when landslides may o...

There is an urgent need for accurate and effective Landslide Early Warning Systems (LEWS). Most LEWS are currently based on a single temporally-aggregated measure of rainfall derived from either in-situ measurements or satellite-based rainfall estimates. Relying on a summary metric of precipitation may not capture the complexity of the rainfall sig...

Seismic waves can shake mountainous landscapes, triggering thousands of landslides. Regional-scale landslide models primarily rely on shaking intensity parameters obtained by simplifying ground motion time-series into peak scalar values. Such an approach neglects the contribution of ground motion phase and amplitude and their variations over space...

The most adopted definition of landslide hazard combines spatial information about landslide location (susceptibility), threat (intensity), and frequency (return period). Only the first two elements are usually considered and estimated when working over vast areas. Even then, separate models constitute the standard, with frequency being rarely inve...

Mountainous landscapes affected by strong earthquakes typically exhibit higher landslide susceptibility in post-seismic periods compared to pre-seismic conditions. This concept is referred to as the earthquake legacy effect, which needs to be better understood to develop an accurate post-seismic landslide hazard assessment. The earthquake legacy ef...

Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency. hazard quantification for extreme wildfires is an important component of wildfire management, as it facilitates efficient resource distribution, a...

The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven probabilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic elements that woul...

Classifying a given landscape on the basis of its susceptibility to surface processes is a standard procedure in low to mid-latitudes. Conversely, these procedures have hardly been explored in periglacial regions. However, global warming is radically changing this situation and will change it even more in the future. For this reason, understanding...

The literature on landslide susceptibility is rich with examples that span a large number of topics. However, the component that pertains to the extension of the susceptibility framework toward space-time modeling is largely unexplored. This statement is even more valid when looking at the landslide risk context, where hardly any scientific contrib...

Classifying a given landscape on the basis of its susceptibility to surface processes is a standard procedure in low to mid-latitudes. Conversely, these procedures have hardly been explored in periglacial regions, primarily because of the limited presence of human settlements and, therefore, the little need for risk assessment. However, global warm...

Strong earthquakes not only induce co-seismic mass wasting but also exacerbates the shear strength of hillslope materials and cause higher landslide susceptibility in the subsequent years following the earthquake. Previous studies have mainly investigated post-seismic landslide activity mainly by using landslide inventories. However, landslide inve...

This manuscript presents an analytical protocol based on explainable AI where the susceptibility to hydro-morphological processes is estimated per catchment at the continental scale. In doing so, we highlight the strength of this approach, for each covariate contribution can be queried and understood at the single mapping unit level. To further e...

Sustainable agricultural management requires knowledge of where and when crops are grown, what they are, and for how long. However, such information is not yet available in Nepal. Remote sensing coupled with farmers’ knowledge offers a solution to fill this gap. In this study, we created a high-resolution (10 m) seasonal crop map and cropping patte...

Ground motion simulations solve wave equations in space and time, thus producing detailed estimates of the shaking time series. This is essentially uncharted territory for geomorphologists, for we have yet to understand which ground motion (synthetic or not) parameter, or combination of parameters, is more suitable to explain the coseismic landslid...

Mountainous landscapes affected by strong earthquakes exhibit relatively higher landslide susceptibility in post-seismic periods compared to pre-seismic conditions. This concept is referred to as the earthquake legacy effect and is mainly examined by monitoring either rapid landslide occurrences or slow-moving landslides over time. To provide a mor...

In order to develop reliable models, the geoscientific community requires high-resolution data sets. However, the collection of such data is a persistent challenge due to the limitations of resources. The concept of super-resolution, a method from the field of machine learning, can be used to predict a high-resolution version of a low-resolution da...

Knowledge of geomorphological processes, their dynamics, and resulting landforms shaped decades of this geoscientific field. The recent advances in technologies for the acquisition of spatial data have brought fundamental changes in increasing the accuracy and frequency of evaluating the rate of geomorphological processes. Digitization, miniaturiza...

For decades, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physics-based models. The part of the geomorphology community focusing on data-driven model has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimated when landslides may occu...

Super Resolution is a method for artificially increasing the imaging system's resolution by post processing without having to collect new datasets. It is mostly developed and used in computer graphics by the computer science community for image and video enhancement due to its capacity to add spatial variations in the data and perform better than c...

A deep neural network (DNN), evolved from a traditional artificial neural network, has been seamlessly adapted for the spatial data domain over the years. Deep learning (DL) has been widely applied for a number of applications and a variety of thematic domains. This article reports on a systematic review of methods adapted in major DNN applications...

The losses due to natural hazards are very high and show an increasing trend due to climate change; human and economic growth; and unplanned development. The risk due to those hazards can be reduced using multi-hazard risk assessment using hazard, the element at risk and vulnerability data. However, due to the lack of good quality and high-resoluti...

Presented at United Nations/ Nepal Workshop on the application of GNSS, mainly focus on how GNSS is useful in managing the Disasters