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In the face of climate change, the assessment of land transport infrastructure exposure towards adverse climate events is of major importance for Europe's economic prosperity and social wellbeing. Robust and reliable information on the extent of climate change and its projected future impacts on roads and railways are of prime importance for proact...
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In the face of climate change, the assessment of land
transport infrastructure exposure towards adverse climate events is of
major importance for Europe's economic prosperity and social wellbeing.
In this study, a climate index estimating rainfall patterns which
trigger landslides in central Europe is analysed until the end of this
century and comp...
Citations
... Rainfall-induced soil slope failures most frequent and widespread damaging landslides in the world, causing significant damage to infrastructure and property, endanger human lives, and impact the natural environment (Hong et al., 2005;Baum and Godt, 2010;Cascini et al., 2011;Lee et al., 2014;Igwe et al., 2014;Calvello et al., 2015;Schlögl and Matulla, 2018;Piciullo et al., 2018;Gao et al., 2018;Lu et al., 2020;Nam and Wang, 2020;Sun et al., 2021;Yang et al., 2022;Rahardjo et al., 2005;Ohta et al., 2010;Zhang et al., 2016). Factors such as the intensity, duration, and frequency of rainfall, as well as soil type, topography, and vegetation cover, all contribute to the likelihood of slope failure (Tohari et al., 2007;Sidle and Bogaard, 2016;Wu et al., 2017;Yubonchit et al., 2017;Gonzalez-Ollauri and Mickovski, 2017;Zhang et al., 2019;Picarelli et al., 2020;Wang et al., 2021;Gallage et al., 2021;Jiang et al., 2022a;Lu et al., 2022). ...
Rainfall-induced soil slope failures are among the most frequent and widespread landslides in the world. The infiltration of water plays a critical role in the instability of slopes under rainfall conditions. By employing the physical models, field monitoring data becomes instrumental for the back analysis of soil parameters, providing critical insights into the mechanisms of slope instability. In this review, the initial section investigates the hydraulic characteristics of unsaturated soils, encompassing the soil-water characteristic curve, permeability function, and their variability nature. Subsequently, a comprehensive overview of infiltration models for unsaturated soil, coupled with their numerical and analytical solutions, is provided. The subsequent section systematically introduces physical model-based Bayesian parameter inference, with a particular emphasis on the formulations of likelihood functions. The exploration then shifts towards investigating the impacts of prior knowledge, likelihood, model structure, and assimilation approach on an unsaturated soil slope monitoring case. Finally, the limitations of the current approaches and future outlooks in this field are presented.
... To assess flood, landslides, and fire hazards, we need to have access to spatial and temporal data to properly model the static and dynamic factors (van Westen et al. 2006). The identified influential factors that have a static nature include topographical aspects (elevation, slope, and aspect), geological factors (rock type, soil depth, and bedrock characteristics), hydrological factors (distance from rivers, drainage density, drainage, and soil moisture), geomorphological aspects (physiographic units and Earth morphologies), and environmental factors (roads and structures) ( Schlögl and Matulla 2018;Hu et al. 2020). Dynamic factors also include rainfall, temperature, humidity, and vegetation characteristics which vary over time and space (Zhu et al. 2014). ...
Golestan National Park is one of the oldest biosphere reserves exposed to environmental hazards due to growing demand, geographical location of the park, mountainous conditions, and developments in the last five decades. This study aimed to evaluate potential environmental hazards using machine-learning techniques. This study applied maximum entropy, random forest, boosted regression tree, generalized additive model, and support vector machine methods to model environmental hazards and evaluated the impact of affecting agents and their area of influence. After data collection and preprocessing, the models were implemented, tuned, and trained, and their accuracies were determined using the “receiver operating characteristic curve”. The results indicate the high importance of climatic and human variables, including rainfall, temperature, presence of shepherds, and villagers for fire hazards, elevation, transit roads, temperature, and rainfall for the formation of floodplains, and elevation, transit roads, rainfall, and topographic wetness index in the occurrence of landslides in the national park. The boosted regression tree model with a “AUC value” of 0.98 for flooding, 0.97 for fire, and 0.93 for landslides hazards, had the best performance. The modeling estimated that, on average, 16.2% of the area of Golestan National Park has a high potential for landslides, 14% has a high potential for fire, and 7.2% has a high potential for flooding. So, results of this study can be applied by land use planners, decision makers, and managers of various organizations to decrease effects of these hazards Golestan National Park (GNP).
... Terrain ruggedness index (TRI) is the mean difference between a central pixel and its surrounding cells and was used here to quantify landscape heterogeneities ( Figure 4h). It is defined as [42]: ...
In some arid regions, groundwater is the only source of water for human needs, so understanding groundwater potential is essential to ensure its sustainable use. In this study, three machine learning models (Genetic Algorithm for Rule-Set Production (GARP), Quick Unbiased Efficient Statistical Tree (QUEST), and Random Forest (RF)) were applied and verified for spatial prediction of groundwater in a mountain bedrock aquifer in Piranshahr Watershed, Iran. A spring location dataset consisting of 141 springs was prepared by field surveys, and from this three different sample datasets (S1–S3) were randomly generated (70% for training and 30% for validation). A total of 10 groundwater conditioning factors were prepared for modeling, namely slope percent, relative slope position (RSP), plan curvature, altitude, drainage density, slope aspect, topographic wetness index (TWI), terrain ruggedness index (TRI), land use, and lithology. The area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) were used to evaluate the accuracy of models. The results indicated that all models had excellent goodness-of-fit and predictive performance, but that RF (AUCmean = 0.995, TSSmean = 0.89) and GARP (AUCmean = 0.957, TSSmean = 0.82) outperformed QUEST (AUCmean = 0.949, TSSmean = 0.74). In robustness analysis, RF was slightly more sensitive than GARP and QUEST, making it necessary to consider several random partitioning options for preparing training and validation groups. The outcomes of this study can be useful in the sustainable management of groundwater resources in the study region.
... Where the frequency and intensity of severe rainfall events is supposed to increase, more landslides will be triggered and exposure to landslide risk will rise in these areas (Gariano & Guzzetti, 2016). In general, the impact of changing climate on rapid and shallow slope instabilities is forecasted to become more severe and widespread across Europe throughout the 21st century (Schlögl & Matulla, 2018). ...
... Guha et al. (2015) concluded that at the beginning of the 21 st century Europe's landslide vulnerable territory is expected to face an increased event of landslide frequency, intensity and fatalities. Schlögl and Matulla (2018) also indicated an overall increase in landslides caused by heavy precipitation in the near (2021-2050) and far future (2071-2100) over the central Europe region (Table 2). Overall, projected changes in heavy precipitation will intensify the occurrence of landslides in some parts of Europe with high confidence (IPCC, 2018). ...
Hydro-meteorological hazards (HMHs) have had a strong impact on human societies and ecosystems. Their impact is projected to be exacerbated by future climate scenarios. HMHs cataloguing is an effective tool to evaluate their associated risks and plan appropriate remediation strategies. However, factors linked to HMHs origin and
triggers remain uncertain, which poses a challenge for their cataloguing. Focusing on key HMHs (floods, storm surge, landslides, droughts, and heatwaves), the goal of this review paper is to analyse and present a classification scheme, key features, and elements for designing nature-based solutions (NBS) and mitigating the adverse impacts of HMHs in Europe. For this purpose, we systematically examined the literature on NBS classification
and assessed the gaps that hinder the widespread uptake of NBS. Furthermore, we critically evaluated the existing literature to give a better understanding of the HMHs drivers and their interrelationship (causing multi-hazards). Further conceptualisation of classification scheme and categories of NBS shows that relatively few studies have been carried out on utilising the broader concepts of NBS in tackling HMHs and that the classification and effectiveness of each NBS are dependent on the location, architecture, typology, green species and environmental conditions as well as interrelated non-linear systems. NBS are often more cost-effective than hard engineering approaches used within the existing systems, especially when taking into consideration their potential co-benefits. We also evaluated the sources of available data for HMHs and NBS, highlighted gaps in data, and presented strategies to overcome the current shortcomings for the development of the NBS for HMHs. We highlighted specific gaps and barriers that need to be filled since the uptake and upscaling studies of NBS in HMHs reduction is rare. The fundamental concepts and the key technical features of past studies reviewed here could help practitioners to design and implement NBS in a real-world situation.
... This paper partly closes this gap by assessing the road network vulnerability of alpine communities to landslide events in the context of rural road networks. Mountain roads, in contrast to lowland areas, are highly vulnerable due a higher probability of climate-driven hazard events and the inherent obstacles of implementing redundant systems Schlögl and Matulla, 2017;Schlögl and Laaha, 2017;Doll 10 et al., 2014;Eisenack et al., 2011). Nevertheless, the relation between infrastructure and communal development in mountain areas is not one-directional, meaning that it is only the former that can impact the latter; instead, the influence is rather two-way (Jaafari et al., 2015). ...
Road networks are complex interconnected systems. Any sudden disruption can result in debilitating impacts on human
life or the economy. In particular, road systems in mountain areas are highly vulnerable, because they often do not feature
redundant elements at comparable efficiency.
This paper addresses the impacts of network interruptions caused by landslide events on the (rural) road network system in Vorarlberg, Austria.
Based on a landslide susceptibility map we demonstrate the performance of agent-based traffic modeling using disaggregated
agent data. This allows gaining comprehensive insights into the impacts of road network interruptions on the mobility behavior
of affected people. Choosing an agent-based activity-chain model enables us to integrate the individual behavioral decision-making processes in the traffic flow model. The detailed representation of individual agents in the transport model allows for
optimizing certain characteristics of agents and to include their social learning effects into the system.
Depending on the location of the interruption, our findings reveal median deviation times ranging between several minutes
and more than half an hour, with effects being more severe for employed people than for unemployed individuals.
Moreover, results show the benefits of using agent-based traffic modeling for assessing the impacts of road network interruptions on rural communities by providing insight into the characteristics of the population affected, as well as the effects on
daily routines in terms of detour costs. This allows hazard managers and policy makers to increase the resilience of rural road
network systems in remote areas.
... The mean difference between a central pixel and its surrounding cells and quantify landscape heterogeneities, which could exert influence on the localisation of the triggering area of shallow landslides ( Różycka et al., 2017;Schlögl and Matulla, 2018). ...
It is of fundamental importance to model the relationship between geo-environmental factors and piping erosion because of the environmental degradation attributed to soil loss. Methods that identify areas prone to piping erosion at the regional scale are limited. The main objective of this research is to develop a novel modeling approach by using three machine learning algorithms—mixture discriminant analysis (MDA), flexible discriminant analysis (FDA), and support vector machine (SVM) in addition to an unmanned aerial vehicle (UAV) images to map susceptibility to piping erosion in the loess-covered hilly region of Golestan Province, Northeast Iran. In this research, we have used 22 geo-environmental indices/factors and 345 identified pipes as predictors and dependent variables. The piping susceptibility maps were assessed by the area under the ROC curve (AUC). Validation of the results showed that the AUC for the three mentioned algorithms varied from 90.32% to 92.45%. We concluded that the proposed approach could efficiently produce a piping susceptibility map.
During the next few decades, changes in rainfall frequency and magnitude are expected to have major impacts on landscape evolution, social, and economic aspects of human society. We focus on seasonal rainfall variations by the end of the twenty-first century to define affected landslide-prone areas, future landslide alerts and the impact of landslides on landscape development in the juncture of the Alpine, Pannonian, and Mediterranean region. A moderate and a worst-case climate scenario from CMIP5 global climate simulations were considered to determine the impact of rainfall on the two most common types of landslides in region, shallow and deep-seated landslides. The observed changes in the occurrence of shallow landslides are significant, especially in the winter months, where we can expect more landslide-prone areas compared to the baseline period. Shallow landslides will have a greater impact on the landscape in spring and summer than deep-seated landslides, especially in vineyards.
At first glance, assessing future landslide‐exposed population appears to be a straightforward task if landslide hazard estimates, climate change, and population projections are available. However, the intersection of landslide hazard with socioeconomic elements may result in significant variation of estimated landslide exposure due to considerable variations in population projections. This study aims to investigate the effects of different sources of population data on the evaluation of landslide‐exposed population in China under four Shared Socioeconomic Pathways (SSPs) scenarios. We utilize multiple global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 and six high‐resolution spatially explicit static and dynamic population data sets to drive available landslide models. The results indicate an overall rise in landslide hazard projections, with an increase in the potential impact area of 0.4%–2.7% and an increase in the landslide frequency of 4.7%–20.1%, depending on the SSPs scenarios and future periods. However, the likely changes in future landslide exposed population, as modeled by incorporating population data from different sources with landslide hazard, yield divergent outcomes depending on the population data source. Thus, some of the projections depict an increase in future landslide exposure, while others show a clear decrease. The nationwide divergence ranged from −64% to +48%. These divergent findings were mainly attributed to differences in population data and a lesser extent to variations in GCMs. The present findings highlight the need to pay closer attention to the dynamic evolution of the elements at risk and the associated data uncertainties.
Weather phenomena can result in severe impacts on railway infrastructure. In future, projected changes to the frequency and/or intensity of extreme weather events could change weather–infrastructure risk profiles. Infrastructure owners and operators need to manage current weather impacts and put in place adequate plans to anticipate and adapt to changes in future weather risks, or mitigate the impacts arising from those risks. The assessment of the risk posed to railway infrastructure from current and future weather is dependent on a good understanding of the constituent components of risk: hazard, vulnerability, and exposure. A good understanding of the baseline and projected future risk is needed in order to understand the potential benefits of various climate change adaptation actions. Traditional risk assessment methods need some modification in order to be applied to climate change timescales, for which decisions need to be made under deep uncertainty. This review paper highlights some key challenges for assessing the risk, including: managing uncertainties; understanding weather‐impact relationships and how they could change with climate change; assessing the costs of current and future weather impacts and the potential cost versus benefit of adaptation; and understanding practices and tools for adapting railway infrastructure. The literature reveals examples of progress and good practice in all these areas, providing scope for effective knowledge‐sharing—across the railway infrastructure and other sectors—in support of infrastructure resilience and adaptation.
This article is categorized under: Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change
