Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

A data-driven evaluation of post-fire landslide susceptibility

Wildfires change the hydrologic and geomorphic response of watersheds, which has been associated with cascading hazards that include shallow landslides and debris flows. This study evaluates post-wildfire landslide trigger characteristics by comparing precipitation preceding landslides at both burned and unburned locations. Landslide events are selected from the NASA Global Landslide Catalog (GLC) to facilitate regional inter-comparison. Fire and precipitation histories for each site are established using MODIS global burned area and CHIRPS precipitation data, respectively. Analysis of normalized seven-day accumulated precipitation for sites across all regions shows that, globally, landslides at burned sites are preceded by less precipitation than landslides without antecedent burn events. This supports the hypothesis that fire increases rainfall-driven landslide hazards. An analysis of the seasonality of landslides at burned and unburned locations shows that landslide-triggering storms in burned locations tend to exhibit different seasonality from other rainfall-triggered landslides, with a variety of seasonal shifts ranging from approximately six months in the Pacific Northwest of North America to one week in the Himalaya region. Overall, this manuscript offers an exploration of regional differences in the characteristics of rainfall-triggered landslides over a broad spatial scale and encompassing a variety of climates, geographies, and burn conditions.

Comparison of length and dynamics of wood pieces in streams covered with coniferous and broadleaf forests mapped using orthophotos acquired by an unmanned aerial vehicle

As wood pieces supplied by landslides and debris flows are one of the main components of ecological and geomorphic systems, the importance of quantifying the dimensions of the wood pieces is evident. However, the low accessibility of disturbed channels after debris flows generally impedes accurate and quick wood-piece investigations. Thus, remote-sensing measurements for wood pieces are necessitated. Focusing on sub-watersheds in coniferous and broadleaf forests in Japan (the CF and BF sites, respectively), we measured the lengths of wood pieces supplied by landslides (> 0.2 m length and > 0.03 m diameter) from orthophotos acquired using a small unmanned aerial vehicle (UAV). The measurement accuracy was analyzed by comparing the lengths derived from the UAV method with direct measurements. The landslides at the CF and BF sites were triggered by extremely heavy rainfalls in 2017 and 2018, respectively. UAV flights were operated during February and September 2019 at the CF site and during November 2018 and December 2019 at the BF site. Direct measurements of wood pieces were carried out on the date of the respective second flight date in each site. When both ends of a wood piece are satisfactorily extracted from an orthophoto acquired by the UAV, the wood-piece lengths at the CF site can be measured with an accuracy of approximately ±0.5 m. At the BF site, most of the extracted lengths were shorter than the directly measured lengths, probably because the complex structures of the root wad and tree crown reduced the visibility. Most wood pieces were discharged from landslide scars at the BF site, but at the CF site, approximately 750 wood pieces remained in the landslide scars approximately 19 months after the landslide occurrence. The number of wood pieces in the landslide scars of the CF site increased with increasing landslide area, suggesting that some wood pieces can be left even if large landslides occur. The lengths and locations of the entrapped wood pieces at both sites were not significantly changed between the two UAV flight dates. However, during this period, the rainfall intensities around the CF site measured by the closest rain-gauge of the Japan Meteorological Agency reached their second highest values from 1976 to 2019, which exceeded the 30-year return period. This suggests that most of the entrapped wood pieces rarely migrated even under intense rainfall.

A multidisciplinary investigation of deep-seated landslide reactivation triggered by an extreme rainfall event: a case study of the Monesi di Mendatica landslide, Ligurian Alps

In November 2016, an extreme rainfall event affected the Ligurian Alps (NW Italy). Consequently, several landslides and debris flows occurred in the upper Tanarello stream basin. In particular, the village of Monesi di Mendatica was severely damaged by two landslide phenomena: the activation of a rotational landslide, which caused the total collapse of two buildings and part of the main road, and the reactivation of a deep-seated planar massive and a complex landslide, which widely fractured most of the buildings in the village. The latter phenomenon was mostly unknown and had never been monitored prior to the 2016 event. Due to the extensive damage, the village of Monesi was completely evacuated, and the road connecting a ski resort area in the upper part of the valley was closed. Furthermore, a potentially dangerous situation related to the eventual progressive evolution of this landslide that could cause a temporary occlusion of the Tanarello stream still remains. For this reason, we defined the landslide behaviour, triggering conditions and chronological evolution leading to the 2016 event using a multidisciplinary approach. This approach consisted of field surveys, satellite DInSAR time series analyses, digital image correlation techniques, rainfall records analyses, postevent monitoring campaigns and subsurface investigation data analyses, and numerical modelling. This multidisciplinary approach enhanced our understanding of this landslide, which is fundamental to better comprehend its behaviour and possible evolution.

An object method to characterize grainflow morphology

<p>Grainflow, a fundamental agent moving sediment from the crest to the base of dune surfaces, leaves a temporary geomorphological signature on the slipfaces of aeolian dunes. The grainflow signature reflects the complex morphodynamical interaction between wind-driven sand transport and gravity-driven grainflow on an inclined surface. The purpose of this study is to present a method to objectively and efficiently delineate grainflow boundaries and characterize their morphology features by processing Digital Elevation Models (DEMs) obtained by terrestrial laser scanner in Matlab and ArcGIS. The method allows large numbers of grainflows to be quickly and objectively delineated and extracted from LiDAR data. As an aid tp subsequent analysis, the process avoids the subjective nature of manual measurement, thereby improving the commensurability of different grainflow regimes in both terrestrial and extraterrestrial environments. The results can be compared with the available grainflows morphology characteristics which are manually measured. The method is presented here in the context of analyzing grainflows and related processes on the slipfaces of dunes, but it is applicable over the broader scope of other forms of slope failure and geophysical flows, such as avalanches, snowslides, landslides, and debris flows.</p>

Massflow—A software for dynamic modeling and risk evaluation of earth-surfaced flow

<p>Massflow is based on the depth-integrated continuum and solved by second-order MacCormack-TVD finite difference method. Shared code and friendly GUI are provided for researchers and engineers. It adopted CPU and GPU accellerated algorithm to improve the efficiency. Now around 1000 people adopted Massflow to do their own research. Based the framework, we have done several insightful simulations of real landslides and debris flows. Meanwhile, we are developing a solution for catchment-based rainfall- flood-debris flow prediction. We will introduce the basic of the software, the mechanism and related model to modeling the real hazards, and the framework and finished work of forecasting of catchment flood or debris flow. </p>

Risk Assessment and Disaster Prevention and Mitigation Strategies for Large-Scale Sediment Disasters

<p>Under climate change impact, the frequency of extreme hydrological events increases. The occurrence of extreme rainfall events may lead to large-scale flooding or sediment disasters resulting in serious property damage and casualties. Large-scale sediment disasters include large-scale landslides and debris flows which are the main types of disasters causing casualties. In Taiwan, during Typhoon Morakot in 2009, the long duration and high-intensity rainfall led to a large-scale sediment disaster resulting in heavy casualties. A disaster with certain magnitude and complexity cannot be coped with a single disaster management approach. In this study, a risk assessment method considering climate change impacts proposed by the Intergovernmental Panel on Climate Change (IPCC) was adopted. By analyzing hazard, exposure, and vulnerability indicators of large-scale sediment disasters in Xinfa catchment of Kaohsiung City, Taiwan, a disaster risk adaptation strategy was proposed based on the impact of disaster factors.</p><p>Two scenarios were applied for the catchment sediment hazards risk assessments including 50-year recurrence period (high frequency and low impact) and extreme scenario (low frequency and high impact). Multiple factors for hazard (impact area of landslides and debris flows), exposure (lifeline roads and land use intensity), and vulnerability (disaster prevention and relief resources and settlement population characteristics) assessments were considered. The correlation factor selection and weighting analysis was calibrated by the 2009 Typhoon Morakot event. All disaster-recorded locations were above moderate risk indicating that the risk assessment method was reasonable. A risk map for Xinfa catchment was completed based on the validated risk assessment model to identify the high-risk settlements. After analyzing the spatial characteristics and disaster risk impact factors of high-risk settlements, both software and hardware disaster prevention measures and adaptation strategies were suggested. According to the analyzed results, although the hardware measures were effective in reducing sediment hazards generally, under extreme hydrologic events, those measures could be ineffective due to limited protection capacity of the engineering facilities. Hence, reducing exposure and vulnerability is essential to deal with the impact of extreme events.</p><p><strong>Keywords</strong>: <strong>Large-scale sediment disasters, Risk assessment, Adaptation strategies</strong></p>