Analysis of labour accidents caused by slope failure during slope cutting and application of a rapid checklist for risk management in Japan

Slope failure during or immediately after slope-cutting can cause fatal accidents. This study analyses the characteristics of labour accidents caused by slope failure during slope-cutting in Japan and presents a countermeasure to prevent accidents caused by slope failure, such as the implementation of a slope guideline by MHLW. A case history conducted during slope-cutting and nailing was presented as an example of the application of the slope guideline to ensure safety. Furthermore, monitoring methods were implemented to gain a quantitative understanding of slope deformation. Geological conditions other than those assumed prior to excavation and small collapses attributed to groundwater are indications of landslide risk. The guideline's quick checklist reflects the slope condition or deformation, allowing the client, designer and contractor to discuss and agree on a quick solution to a problem. The case study confirmed the effectiveness of the slope guideline as a tool for sharing information during construction.

The potential of non-native tree species to provide major ecosystem services in Austrian forests

Forestry is facing an unprecedented challenging time. Due to climate change, major tree species, which until recently fulfilled major ecosystem services, are being lost and it is often unclear if forest conversion with other native or non-native tree species (NNT) are able to maintain or restore the endangered ecosystem services. Using data from the Austrian Forest Inventory, we analysed the current and future (2081-2100, RCP 4.5 and RCP 8.5) productivity of forests, as well as their protective function (avalanches and rockfall). Five different species change scenarios were considered for the replacement of a tree species failing in the future. We used seven native tree species (Picea abies, Abies alba, Pinus sylvestris, Larix decidua, Fagus sylvatica, Quercus robur and Quercus petraea) and nine NNT (Pseudotsuga menziesii, Abies grandis, Thuja plicata, Pinus radiata, Pinus contorta, Robinia pseudoacacia, Quercus rubra, Fraxinus pennsylvanica and Juglans nigra). The results show that no adaptation would lead to a loss of productivity and a decrease in tree species richness. The combined use of native and NNT is more favorable than purely using native species in terms of productivity and tree species richness. The impact of the different species change scenarios can vary greatly between the different environmental zones of Austria (Alpine south, Continental and Pannonian). The Pannonian zone would benefit from the use of NNT in terms of timber production. For the protection against avalanches or rockfall in alpine regions, NNT would not be an advantage, and it is more important if broadleaved or coniferous trees are used. Depending on whether timber production, protective function or tree species richness are considered, different tree species or species change scenarios can be recommended. Especially in protective forests, other aspects are essential compared to commercial forests. Our results provide a basis for forest owners/managers in three European environmental zones to make decisions on a sustainable selection of tree species to plant in the face of climate change.

Evaluation of influences of forest cover change on landslides by comparing rainfall-induced landslides in Japanese artificial forests with different ages

In this study, we evaluated the influence of forest cover changes on rainfall-induced shallow landslides by comparing two shallow landslides and debris flows that occurred on plantation forests of different ages in Japan: the Kake disaster in 1988 and the Asakura disaster in 2017. At Kake, the trees ranged in age from 10 to 30 years, whereas at Asakura the trees were over 40 years old. The rainfall characteristics that triggered each landslide were estimated using a three-layer tank model, and the results, as well as the volume of driftwood produced by the landslides, were then compared. Both landslides occurred when the first tank storage layer value, corresponding to the temporal variation in groundwater level in the shallow soil layer, exceeded its previous maximum. The return period of this value at the time of the landslides was 3.0-fold higher in the more mature forests of Asakura than in the young forests of Kake. The upper limit of driftwood volume was 30-fold higher in Asakura than in Kake. Our findings indicated that shallow landslides and debris flows become increasingly rare as forests mature; however, the large volume of driftwood produced by landslides in mature forests may cause substantial damage when extreme rainfall events exceed the landslide resistance of those forests. These insights may be applied to effective landslide risk management.

Climate change amplified the 2009 extreme landslide event in Austria

Landslides are an important natural hazard in mountainous regions. Given the triggering and preconditioning by meteorological conditions, it is known that landslide risk may change in a warming climate, but whether climate change has already affected individual landslide events is still an open question, partly owing to landslide data limitations and methodological challenges in climate impact attribution. Here, we demonstrate the substantial influence of anthropogenic climate change on a severe event in the southeastern Alpine forelands with some estimated 952 individual landslides in June 2009. Our study is based on conditional event attribution complemented by an assessment of changes in atmospheric circulation. Using this approach, we simulate the meteorological event under observed and a range of counterfactual conditions of no climate change and explicitly predict the landslide occurrence probability for these conditions. We find that up to 10%, i.e., 95 landslides, can be attributed to climate change.

Deep learning forecast of rainfall-induced shallow landslides

Rainfall triggered landslides occur in all mountain ranges posing threats to people and the environment. Given the projected climate changes, the risk posed by landslides is expected to increase, and the ability to anticipate their occurrence is key for effective risk reduction. Empirical thresholds and physically-based models are used to anticipate the short-term occurrence of rainfall-induced shallow landslides. But, evidence suggests that they may not be effective for operational forecasting over large areas. We propose a deep-learning based strategy to link rainfall to landslide occurrence. We inform and test the system with rainfall and landslide data available for the last 20 years in Italy. Our results indicate that it is possible to anticipate effectively the occurrence of rainfall-induced landslides over large areas, and that their location and timing are controlled primarily by the precipitation, opening to the possibility of operational landslide forecasting based on rainfall measurements and quantitative meteorological forecasts.

Determining Flood Zonation Maps, Using New Ensembles of Multi-Criteria Decision-Making, Bivariate Statistics, and Artificial Neural Network

Golestan Province is one of the most vulnerable areas to catastrophic flood events in Iran. The flood severity in this region has grown dramatically during the last decades, demanding a major investigation. Accordingly, an authentic map providing detailed information on floods is required to reduce future flood disasters. Three ensemble models produced by the combination of Evaluation Based on Distance from Average Solution (EDAS) and Multilayer Perceptron Neural Network (MLP) with Frequency Ratio (FR), and Weights of Evidence (WOE) are used to quantify the map flood susceptibility in Golestan Province, in the north of Iran. Ten flood effective criteria, namely altitude, slope degree, slope aspect, plan curvature, distance from rivers, Topographic Wetness Index (TWI), rainfall, soil type, geology, and land use, are considered for the modeling process. The flood zonation maps are validated by the receiver operating curve (ROC). The results show that the most precise model is MLP-FR (AUROC = 0.912), followed by EDAS-FR-AHP (AUROC = 0.875), and EDAS-WOE-AHP (AUROC = 0.845). The high accuracies of all methods applied to illustrate their capability in predicting flood susceptibility in future studies.

A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia

Despite great advances in remote sensing technologies, accurate satellite information is sometimes challenged in tropical regions where dense vegetation prevents the instruments from retrieving reliable readings. In this work, we introduce a satellite-based landslide rainfall threshold for the country of Colombia by studying 4 years of rainfall measurements from The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) for 346 rainfall-triggered landslide events (the dataset). We isolate the two successive rainy/dry periods leading to each landslide to create variables that simulate the dynamics of antecedent wetness and dryness. We test the performance of the derived variables (Rainfall Period 1 (PR1), Rainfall Sum 1 (RS1), Rainfall Period 2 (PR2), Rainfall Sum 2 (RS2), and Dry Period (DT)) in a logistic regression that includes three (3) static parameters (Soil Type (ST), Landcover (LC), and Slope angle). Results from the logistic model describe the influence of each variable in landslide occurrence with an accuracy of 73%. Subsequently, we use these dynamic variables to model a landslide threshold that, in the absence of satellite antecedent soil moisture data, helps describe the interactions between the dynamic variables and the slope angle. We name it the Landslide Triggering Factor—LTF. Subsequently, with a training dataset (65%) and one for testing (35%) we evaluate the LTF threshold performance and compare it to the well-known event duration (E-D) threshold. Results demonstrate that The LTF performs better than the E-D threshold for the training and testing datasets at 71% and 81% respectively.

Spatio-temporal evolution of post-seismic landslides and debris flows: 2017 Ms 7.0 Jiuzhaigou earthquake

Spatio-temporal evolution of post-seismic landslides and debris flows provides a new perspective to understand post-earthquake evolution of geological environments and landscapes, and to instruct cascaded catastrophic hazard mitigation and post-disaster reconstruction. However, limited earthquake events have been investigated for post-earthquake geohazard evolution. This work reports the geohazard evolution after the 2017 M_s 7.0 Jiuzhaigou earthquake considering the effects of the earthquake, geology, terrain, meteorology, hydrology, and human engineering activity. Some new viewpoints are suggested. (1) Landslide and debris flow activity intensified in the first year following the earthquake under the effects of the antecedent earthquake, precipitation, fault tectonics, human engineering activity, and fluvial networks. (2) Landslide and debris flow activity declined rapidly in the second year as a result of dramatically reduced sediments, declined rainfall, and self-healed slopes. (3) The significant decay of landslide and debris flow activity and the prominent reduction of loose deposits indicate that the geological environment was gradually restoring. (4) Although the hazard effect mitigation and geological environment restoration were ongoing (in the absence of rainstorm events) to attain a new balance, the geoenvironment has not returned to the pre-earthquake level because of widespread unrecovered geohazards and the remaining loose deposits on hillslopes or in channels. (5) The geological environment after the Jiuzhaigou earthquake may re-equilibrate and return to the pre-earthquake level more quickly than after the Kashmir, Chi-Chi, Gorkha, Wenchuan, and Murchison earthquakes. This work provides new knowledge pertaining to geohazard evolution after a strong earthquake and to profound impacts of a catastrophic earthquake on geological environment and landscape.

Multi-hazard spatial modeling via ensembles of machine learning and meta-heuristic techniques

Considering the large number of natural disasters on the planet, many areas in the world are at risk of these hazards; therefore, providing an integrated map as a guide map for multiple natural hazards can be applied to save human lives and reduce financial losses. This study designed a multi-hazard map for three important hazards (earthquakes, floods, and landslides) to identify endangered areas in Kermanshah province located in western Iran using ensemble SWARA-ANFIS-PSO and SWARA-ANFIS-GWO models. In the first step, flood and landslide inventory maps were generated to identify at-risk areas. Then, the occurrence places for each hazard were divided into two groups for training susceptibility models (70%) and testing the models applied (30%). Factors affecting these hazards, including altitude, slope aspect, slope degree, plan curvature, distance to rivers, distance to roads, distance to the faults, rainfall, lithology, and land use, were used to generate susceptibility maps. The SWARA method was used to weigh the subclasses of the influencing factors in floods and landslides. In addition, a peak ground acceleration (PGA) map was generated to investigate earthquakes in the study area. In the next step, the ANFIS machine learning algorithm was used in combination with PSO and GWO meta-heuristic algorithms to train the data, and SWARA-ANFIS-PSO and SWARA-ANFIS-GWO susceptibility maps were separately generated for flood and landslide hazards. The predictive ability of the implemented models was validated using the receiver operating characteristics (ROC), root mean square error (RMSE), and mean square error (MSE) methods. The results showed that the SWARA-ANFIS-PSO ensemble model had the best performance in generating flood susceptibility maps with ROC = 0.936, RMS = 0.346, and MSE = 0.120. Furthermore, this model showed excellent results (ROC = 0.894, RMS = 0.410, and MSE = 0.168) for generating a landslide map. Finally, the best maps and PGA map were combined, and a multi-hazard map (MHM) was obtained for Kermanshah Province. This map can be used by managers and planners as a practical guide for sustainable development.

Strategic protection of landslide vulnerable mountains for biodiversity conservation under land-cover and climate change impacts

Landslides are triggered more often by human-induced changes, such as deforestation, infrastructure building, and increasing precipitation extremes, because of climate change. The huge economic and societal loss calls for a more cost-effective way to reduce risks and ensure sustainable development. Land-cover and land-use changes not only increase landslide susceptibility but also drive habitat loss and species extinctions. The high spatial overlap between landslide susceptibility and biodiversity in mountains provides an opportunity to achieve co-benefits in conservation and development. The identification of vulnerable mountains with both high biodiversity and landslide susceptibility prioritizes the regions for expansion of protected areas, forest conservation, and restoration, providing a nature-based solution to mitigate landslide risks through the protection of natural habitat.

Natural disasters impose huge uncertainty and loss to human lives and economic activities. Landslides are one disaster that has become more prevalent because of anthropogenic disturbances, such as land-cover changes, land degradation, and expansion of infrastructure. These are further exacerbated by more extreme precipitation due to climate change, which is predicted to trigger more landslides and threaten sustainable development in vulnerable regions. Although biodiversity conservation and development are often regarded as having a trade-off relationship, here we present a global analysis of the area with co-benefits, where conservation through expanding protection and reducing deforestation can not only benefit biodiversity but also reduce landslide risks to human society. High overlap exists between landslide susceptibility and areas of endemism for mammals, birds, and amphibians, which are mostly concentrated in mountain regions. We identified 247 mountain ranges as areas with high vulnerability, having both exceptional biodiversity and landslide risks, accounting for 25.8% of the global mountainous areas. Another 31 biodiverse mountains are classified as future vulnerable mountains as they face increasing landslide risks because of predicted climate change and deforestation. None of these 278 mountains reach the Aichi Target 11 of 17% coverage by protected areas. Of the 278 mountains, 52 need immediate actions because of high vulnerability, severe threats from future deforestation and precipitation extremes, low protection, and high-population density and anthropogenic activities. These actions include protected area expansion, forest conservation, and restoration where it could be a cost-effective way to reduce the risks of landslides.

Modelling of stress transfer in root-reinforced soils informed by four-dimensional X-ray computed tomography and digital volume correlation data

Vegetation enhances soil shearing resistance through water uptake and root reinforcement. Analytical models for soils reinforced with roots rely on input parameters that are difficult to measure, leading to widely varying predictions of behaviour. The opaque heterogeneous nature of rooted soils results in complex soil–root interaction mechanisms that cannot easily be quantified. The authors measured, for the first time, the shear resistance and deformations of fallow, willow-rooted and gorse-rooted soils during direct shear using X-ray computed tomography and digital volume correlation. Both species caused an increase in shear zone thickness, both initially and as shear progressed. Shear zone thickness peaked at up to 35 mm, often close to the thickest roots and towards the centre of the column. Root extension during shear was 10–30% less than the tri-linear root profile assumed in a Waldron-type model, owing to root curvature. Root analogues used to explore the root–soil interface behaviour suggested that root lateral branches play an important role in anchoring the roots. The Waldron-type model was modified to incorporate non-uniform shear zone thickness and growth, and accurately predicted the observed, up to sevenfold, increase in shear resistance of root-reinforced soil.

Temporal Dynamics of Root Reinforcement in European Spruce Forests

The quantification of post-disturbance root reinforcement (RR) recovery dynamics is of paramount importance for the optimisation of forest ecosystem services and natural hazards risk management in mountain regions. In this work we analyse the long-term root reinforcement dynamic of spruce forests combining data of the Swiss National Forest Inventory with data on root distribution and root mechanical properties. The results show that root reinforcement recovery depends primarily on stand altitude and slope inclination. The maximum root reinforcement recovery rate is reached at circa 100 years. RR increases continuously with different rates for stand ages over 200 years. These results shows that RR in spruce stands varies considerably depending on the local conditions and that its recovery after disturbances requires decades. The new method applied in this study allowed for the first time to quantify the long term dynamics of RR in spruce stands supporting new quantitative approaches for the analysis of shallow landslides disposition in different disturbance regimes of forests.

Evaluation of multi-hazard map produced using MaxEnt machine learning technique

Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gullies was prepared using field surveys and different official reports. To generate the susceptibility maps, a total of 17 geo-environmental factors were selected as predictors using the MaxEnt (Maximum Entropy) machine learning technique. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic-ROC curves and calculating the area under the ROC curve-AUCROC. The MaxEnt model not only implemented superbly in the degree of fitting, but also obtained significant results in predictive performance. Variables importance of the three studied types of hazards showed that river density, distance from streams, and elevation were the most important factors for flood, respectively. Lithological units, elevation, and annual mean rainfall were relevant for detecting landslides. On the other hand, annual mean rainfall, elevation, and lithological units were used for gully erosion mapping in this study area. Finally, by combining the flood, landslides, and gully erosion susceptibility maps, an integrated multi-hazard map was created. The results demonstrated that 60% of the area is subjected to hazards, reaching a proportion of landslides up to 21.2% in the whole territory. We conclude that using this type of multi-hazard map may be a useful tool for local administrators to identify areas susceptible to hazards at large scales as we demonstrated in this research.

Controls on the size distributions of shallow landslides

Rainfall-triggered shallow landslides are destructive hazards and play an important role in landscape processes. A theory explaining the size distributions of such features remains elusive. Prior work connects size distributions to topography, but field-mapped inventories reveal pronounced similarities in the form, mode, and spread of distributions from diverse landscapes. We analyze nearly identical distributions occurring in the Oregon Coast Range and the English Lake District, two regions of strikingly different topography, lithology, and vegetation. Similarity in minimum sizes at these sites is partly explained by theory that accounts for the interplay of mechanical soil strength controls resisting failure. Maximum sizes, however, are not explained by current theory. We develop a generalized framework to account for the entire size distribution by unifying a mechanistic slope stability model with a flexible spatial-statistical description for the variability of hillslope strength. Using hillslope-scale numerical experiments, we find that landslides can occur not only in individual low strength areas but also across multiple smaller patches that coalesce. We show that reproducing observed size distributions requires spatial strength variations to be strongly localized, of large amplitude, and a consequence of multiple interacting factors. Such constraints can act together with the mechanical determinants of landslide initiation to produce size distributions of broadly similar character in widely different landscapes, as found in our examples. We propose that size distributions reflect the systematic scale dependence of the spatially averaged strength. Our results highlight the critical need to constrain the form, amplitude, and wavelength of spatial variability in material strength properties of hillslopes.

Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms

Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km2 were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km2 at the 2 year return period and 40 km2 at the 100 year return period for the high landslide hazard zone, and 6 km2 at the 2 year return period and 20 km2 at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km2 to 62 km2). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.

Landslides in the Andes: Forests can provide cost-effective landslide regulation services

Landslides cause billions of dollars (USD) in damage and hundreds of life losses every year in mountainous areas globally, and these effects are exacerbated by climate change and increased human occupation of vulnerable areas. In many mountainous regions forests deliver slope stability, helping to prevent landslides. However, forests are progressively converted into other land uses in many mountainous regions. In this study, we focus on the Colombian Andes, the most populated and deadly landslide-prone part of Colombia. We aim to determine the difference in frequency of landslides from forested and non-forested areas, and subsequently, quantify the potential costs and benefits of protecting forest and of restoring forest from agricultural lands. To that end, we combine economic data with geographical information related to public and private infrastructure, land use, and landslide susceptibility. Analyzing the national landslide database of Colombia, we established that landslides are almost six times (581%) more likely to occur on non-forested lands than on forested lands. From an economic perspective, by preventing landslides, forests provide a net benefit through the provision of slope stability services. Our most conservative estimates indicate it is 16 times more cost-effective to promote forest corridors, via conservation or reforestation along roads by paying farmers and cattle herders their opportunity costs, than for the public to pay the expected value of landslide damage. Our analysis provides strong evidence that vegetated hillsides can provide a cost-effective ecosystem service approach to mitigate economic losses due to landslides in one of the world's most landslide prone areas.

Re-activation of landslide in sub-Arctic areas due to extreme rainfall and discharge events (the mouth of the Great Whale River, Nunavik, Canada)

Climate change is impacting surficial geomorphic processes, especially in sensitive areas such as the sub-Arctic. One of the most common examples involves landslides, which often develop in glacio-isostatically raised marine clays in northeastern Canada. One of these sites, an expansive area of complex landslide terrain located at the mouth of the Great Whale River in Nunavik, has already been studied due to its age and morphology. We present new data, based on the multidisciplinary research including geomorphic, dendrochronological, and hydroclimatological analyses, allowing us to determine how contemporary climate change has affected landslide reactivation during the last 80 years. Our research included collecting 60 cores from Picea glauca trees, growing on the marginal zone of a landslide deposit, as well as from a reference site. The tilted trees formed eccentric growth-ring patterns, which provided us with reliable dates on the landslide events. In addition to these dendrochronological data, we studied these landslides using repeated aerial photography, which showed changes in river channel constrictions in the period 1969-2019. Based on the eccentricity index of the tree ring data, we recognized disturbance events due to landslides. We compared these data with the hydroclimatological conditions and found clearly visible correlations between heavy rainfall and discharge (>95th percentile) of the Great Whale River. The increased landslide activity over the past several years can be linked to an increase in extreme summertime rainfall events. Increased landslide activity poses a real threat, through its input of large amounts of fine-grained sediment to the river, causing it to narrow.

Physical Model Experiments on Water Infiltration and Failure Modes in Multi-Layered Slopes under Heavy Rainfall

To assess the influence of an intermediate coarse layer on the slope stability during heavy rainfall, knowledge about water movement and how slope failure occurs is important. To clarify the characteristics of water infiltration in a multi-layered slope and assess its influence on the slope failure modes, eight groups of physical slope models were investigated. It was found that the unsaturated hydraulic conductivity in the coarse layer (5.54 × 10−6 cm/s) was much lower than that of the fine layer (1.08 × 10−4 cm/s), which resulted in the capillary barrier working at a lower water content. Intermediate coarse layers embedded between finer ones may initially confine the infiltration within the overlying finer layers, delaying the infiltration and eventually inducing a lateral flow diversion in the inclined slope. Two different failure modes occurred in the model experiments: surface sliding occurred at the toe in the single-layer slope group and piping occurred at the toe in the multi-layered slope as the rainfall water accumulated, was diverted along the interface, and then broke through in the downslope direction of the intermediate coarse layer. The lateral flow diversion caused by the capillary barrier and the tilt angle may be the major factors influencing the difference of the failure modes. The result also revealed that the coarser layers may have negative effects on the slope stability.

Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios

Soil erosion is a serious problem affecting numerous countries, especially, gully erosion. In the current research, GIS techniques and MARS (Multivariate Adaptive Regression Splines) algorithm were considered to evaluate gully erosion susceptibility mapping among others. The study was conducted in a specific section of the Gorganroud Watershed in Golestan Province (Northern Iran), covering 2142.64 km2 which is intensely influenced by gully erosion. First, Google Earth images, field surveys, and national reports were used to provide a gully-hedcut evaluation map consisting of 307 gully-hedcut points. Eighteen gully erosion conditioning factors including significant geoenvironmental and morphometric variables were selected as predictors. To model sensitivity of gully erosion, Multivariate Adaptive Regression Splines (MARS) was used while the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), drawing ROC curves, efficiency percent, Yuden index, and kappa were used to evaluate model efficiency. We used two different scenarios of the combination of the number of replications, and sample size, including 90%/10% and 80%/20% with 10 replications, and 70%/30% with five, 10, and 15 replications for preparing gully erosion susceptibility mapping (GESM). Each one involves a various subset of both positive (presence), and negative (absence) cases. Absences were extracted as randomly distributed individual cells. Therefore, the predictive competency of the gully erosion susceptibility model and the robustness of the procedure were evaluated through these datasets. Results did not show considerable variation in the accuracy of the model, with altering the percentage of calibration to validation samples and number of model replications. Given the accuracy, the MARS algorithm performed excellently in predictive performance. The combination of 80%/20% using all statistical measures including SST (0.88), SPF (0.83), E (0.79), Kappa (0.58), Robustness (0.01), and AUC (0.84) had the highest performance compared to the other combinations. Consequently, it was found that the performance of MARS for modelling gully erosion susceptibility is quite consistent while changes in the testing and validation specimens are executed. The intense acceptable prediction capability of the MARS model verifies the reliability of the method employed for use of this model elsewhere and gully erosion studies since they are qualified to quickly generating precise and exact GESMs (gully erosion sensitivity maps) to make decisions and management edaphic and hydrologic features.

Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations

In the past decades, many different approaches have been developed in the literature to quantify the load-carrying capacity and geotechnical stability (or the Factor of Safety, F _s) of variably saturated hillslopes. Much of this work has focused on a deterministic characterization of hillslope stability. Yet, simulated F _s values are subject to considerable uncertainty due to our inability to characterize accurately the soil mantle's properties (hydraulic, geotechnical and geomorphologic) and spatiotemporal variability of the moisture content of the hillslope interior. This is particularly true at larger spatial scales. Thus, uncertainty-incorporating analyses of physically based models of rain-induced landslides are rare in the literature. Such landslide modeling is typically conducted at the hillslope scale using gauge-based rainfall forcing data with rather poor spatiotemporal coverage. For regional landslide modeling, the specific advantages and/or disadvantages of gauge-only, radar-merged and satellite-based rainfall products are not clearly established. Here, we compare and evaluate the performance of the Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) model for three different rainfall products using 112 observed landslides in the period between 2004 and 2011 from the North Carolina Geological Survey database. Our study includes the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis Version 7 (TMPA V7), the North American Land Data Assimilation System Phase 2 (NLDAS-2) analysis, and the reference 'truth' Stage IV precipitation. TRIGRS model performance was rather inferior with the use of literature values of the geotechnical parameters and soil hydraulic properties from ROSETTA using soil textural and bulk density data from SSURGO (Soil Survey Geographic database). The performance of TRIGRS improved considerably after Bayesian estimation of the parameters with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm using Stage IV precipitation data. Hereto, we use a likelihood function that combines binary slope failure information from landslide event and 'null' periods using multivariate frequency distribution-based metrics such as the False Discovery and False Omission Rates. Our results demonstrate that the Stage IV-inferred TRIGRS parameter distributions generalize well to TMPA and NLDAS-2 precipitation data, particularly at sites with considerably larger TMPA and NLDAS-2 rainfall amounts during landslide events than null periods. TRIGRS model performance is then rather similar for all three rainfall products. At higher elevations, however, the TMPA and NLDAS-2 precipitation volumes are insufficient and their performance with the Stage IV-derived parameter distributions indicate their inability to accurately characterize hillslope stability.

Ecosystem changes following the 2016 Kumamoto earthquakes in Japan: Future perspectives

Major earthquakes cause widespread environmental and socioeconomic disruptions that persist for decades. Extensive ground disturbances that occurred during the shallow-focus Kumamoto earthquakes will affect future sustainability of ecosystem services west of Aso volcano. Numbers of earthquake-initiated landslides per unit area were higher in grasslands than forests, likely owing to greater root reinforcement of trees, and mostly initiated on ridgelines and/or convex/planar hillslopes. Most landslides traveled short distances and did not initially evolve into debris flows; resultant sediments and wood accumulating in headwater channels can be mobilized into debris flows during future storms. Fissures along ridgelines may promote water ingress and induce future landslides and debris flows that affect residents downstream. Native grasses are at risk because of habitat fragmentation caused by ground disturbances, extensive damage to rural roads, and abandonment of traditional pasture management practices. Sustainable management of affected areas needs to consider future risk of cascading hazards and long-term socioeconomic impacts.

The Potential Role of Tree Diversity in Reducing Shallow Landslide Risk

Recently, interest in utilizing ecosystems for disaster risk reduction has increased, even though there remains considerable uncertainty regarding the role of ecosystems in buffering against natural hazards. This ecosystem role can be considered an ecosystem service. Although a strong body of evidence shows that biodiversity enhances ecosystem services, there are only a few studies of the relationship between biodiversity and the role of the ecosystem in reducing the risk of natural disasters. To explore the desired state of an ecosystem for disaster risk reduction we applied the finding that biodiversity enhances ecosystem services to evaluate the role of woody vegetation in reducing the frequency and severity of shallow landslides. Using information related to shallow landslides and woody vegetation in Japan as a case study, we compared the severity of shallow landslides (i.e., landslide volume) with tree species richness. Although we provide no direct evidence that tree species richness reduces shallow landslide volume, we found that the predictability of the model, which evaluated relationships between landslide volume and environmental variables in watersheds throughout the Japanese Archipelago, increased with tree species richness. This finding suggests that biodiversity is likely associated with shallow landslide risk reduction, emphasizing a possible reduction of spatial and temporal uncertainty in the roles of woody vegetation. Our study identifies a need for socioecological systems to build new approaches found on the functionality of such ecosystems.

The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy)

The spatial distribution of landslides is influenced by different climatic conditions and environmental settings including topography, morphology, hydrology, lithology, and land use. In this work, we have attempted to evaluate the influence of land use change on landslide susceptibility (LS) for a small study area located in the southern part of the Briga catchment, along the Ionian coast of Sicily (Italy). On October 1, 2009, the area was hit by an intense rainfall event that triggered abundant slope failures and resulted in widespread erosion. After the storm, an inventory map showing the distribution of pre-event and event landslides was prepared for the area. Moreover, two different land use maps were developed: the first was obtained through a semi-automatic classification of digitized aerial photographs acquired in 1954, the second through the combination of supervised classifications of two recent QuickBird images. Exploiting the two land use maps and different land use scenarios, LS zonations were prepared through multivariate statistical analyses. Differences in the susceptibility models were analyzed and quantified to evaluate the effects of land use change on the susceptibility zonation. Susceptibility maps show an increase in the areal percentage and number of slope units classified as unstable related to the increase in bare soils to the detriment of forested areas.

Broader perspective on ecosystem sustainability: consequences for decision making

Although the concept of ecosystem sustainability has a long-term focus, it is often viewed from a static system perspective. Because most ecosystems are dynamic, we explore sustainability assessments from three additional perspectives: resilient systems; systems where tipping points occur; and systems subject to episodic resetting. Whereas foundations of ecosystem resilience originated in ecology, recent discussions have focused on geophysical attributes, and it is recognized that dynamic system components may not return to their former state following perturbations. Tipping points emerge when chronic changes (typically anthropogenic, but sometimes natural) push ecosystems to thresholds that cause collapse of process and function and may become permanent. Ecosystem resetting occurs when episodic natural disasters breach thresholds with little or no warning, resulting in long-term changes to environmental attributes or ecosystem function. An example of sustainability assessment of ecosystem goods and services along the Gulf Coast (USA) demonstrates the need to include both the resilient and dynamic nature of biogeomorphic components. Mountain road development in northwest Yunnan, China, makes rivers and related habitat vulnerable to tipping points. Ecosystems reset by natural disasters are also presented, emphasizing the need to understand the magnitude frequency and interrelationships among major disturbances, as shown by (i) the 2011 Great East Japan Earthquake and resulting tsunami, including how unsustainable urban development exacerbates geodisaster propagation, and (ii) repeated major earthquakes and associated geomorphic and vegetation disturbances in Papua New Guinea. Although all of these ecosystem perturbations and shifts are individually recognized, they are not embraced in contemporary sustainable decision making.

Towards better design and management of tsunami evacuation routes: a case study of Ao Jak Beach Road

Among the thousands of people killed or reported missing in Thailand during the 2004 Indian Ocean Tsunami were villagers in small communities on the Andaman Coast. A combination of factors contributed to loss of life, including the lack of defined evacuation routes. This vulnerability to tsunami attacks has recently been addressed with the demarcation of evacuation routes, along both well-maintained arteries and native surface (unpaved) roads. However, poor location design and irregular maintenance will reduce the lifetime that the latter can provide safe egress from remote coastlines. In this work we identified 10 major gullies and 18 landslides along a critical 0.5 km section of a tsunami evacuation road accessing a remote beach of the Andaman Coast in southern Thailand. Erosion rates from landslides and gullies approached 9500 Mg ha−1 in less than a year following widening of the road. Importantly, the degradation features, landslides in particular, reduced the effectiveness of the road to serve as a safe passageway to escape future tsunamis or large storm surges. This study demonstrates that greater attention should be given to appropriate road location, design and maintenance in integrated programmes aimed at reducing tsunami vulnerability in remote coastal areas, not only on the Andaman Coast, but worldwide.

Linking carbon supply to root cell-wall chemistry and mechanics at high altitudes in Abies georgei

BACKGROUND AND AIMS

The mobile carbon supply to different compartments of a tree is affected by climate, but its impact on cell-wall chemistry and mechanics remains unknown. To understand better the variability in root growth and biomechanics in mountain forests subjected to substrate mass movement, we investigated root chemical and mechanical properties of mature Abies georgei var. smithii (Smith fir) growing at different elevations on the Tibet-Qinghai Plateau.

METHODS

Thin and fine roots (0·1-4·0 mm in diameter) were sampled at three different elevations (3480, 3900 and 4330 m, the last corresponding to the treeline). Tensile resistance of roots of different diameter classes was measured along with holocellulose and non-structural carbon (NSC) content.

KEY RESULTS

The mean force necessary to break roots in tension decreased significantly with increasing altitude and was attributed to a decrease in holocellulose content. Holocellulose was significantly lower in roots at the treeline (29·5 ± 1·3 %) compared with those at 3480 m (39·1 ± 1·0 %). Roots also differed significantly in NSC, with 35·6 ± 4·1 mg g(-1) dry mass of mean total soluble sugars in roots at 3480 m and 18·8 ± 2·1 mg g(-1) dry mass in roots at the treeline.

CONCLUSIONS

Root mechanical resistance, holocellulose and NSC content all decreased with increasing altitude. Holocellulose is made up principally of cellulose, the biosynthesis of which depends largely on NSC supply. Plants synthesize cellulose when conditions are optimal and NSC is not limiting. Thus, cellulose synthesis in the thin and fine roots measured in our study is probably not a priority in mature trees growing at very high altitudes, where climatic factors will be limiting for growth. Root NSC stocks at the treeline may be depleted through over-demand for carbon supply due to increased fine root production or winter root growth.

On the impact of urban landslides

Although considerable attention has been paid to the issue of urban landslides in the literature since Alexander's 1989 paper on the topic, few attempts have been made to quantify their importance. In this paper, such an attempt is made, based upon global landslide fatality data from 2005. First, a new definition for the term ‘urban landslide’ is proposed. This is then applied in the analysis of the 2005 landslide fatality data. It is shown that fatal urban landslides occur primarily in the tropical regions, whereas rural landslides dominate the mid- and high-latitude regions. The causes for this are explored using a detailed dataset for landslide fatalities in Nepal during the period 1968–2006. It is proposed that the key difference between the tropical and the extra-tropical regions is that in the case of the former areas, landslides are mostly triggered as a result of disturbance of the thick weathering layers, whereas in the latter case they tend to be shallow, high-energy events triggered by seasonal rainfall. Finally, an analysis is presented of the distribution by latitude of papers presented in the ‘Urban Landslides’ session at the International Association for Engineering Geology and the Environment (IAEG) Congress in 2006. It is shown that the distribution of papers mirrors the distribution of all landslide types, and of the global population, and not the distribution of urban landslides. This suggests that urban landslides in the tropical regions, specifically, are not receiving adequate attention from the research community.