Hazard Maps Research Articles

Historical Hazard Assessment of Climate and Land Use–Land Cover Effects on Soil Erosion Using Remote Sensing: Case Study of Oman

Human activities, climate change, and land-use alterations accelerated soil erosion in recent decades and imposed significant threats to soil fertility and stability worldwide. Understanding and quantifying the spatiotemporal variation of soil erosion risks is crucial for adopting the best management practices for surface soils conservation. Here, we present a novel high-resolution (30 m) soil erosion framework based on the G2 erosion model by integrating satellite and reanalysis datasets and Machine Learning (ML) models to assess soil erosion risks and hazards spatiotemporally. The proposed method reflects the impacts of climate change in 1 h time resolutions and land use in 30 m scales on soil erosion risks for almost 4 decades (between 1985 and 2017). The soil erosion hazardous maps were generated/evaluated using Extreme Value Analysis (EVA), utilizing long-term annual soil erosion estimations/projections to aid policymakers in developing management strategies to protect lands against extreme erosion. The proposed framework is evaluated in the Sultanate of Oman, which lacks soil erosion estimation/assessment studies due to data scarcity. Results indicate that soil erosion has increasing perilous trends in high altitudes of the Sultanate of Oman that may cause substantial risks to soil health and stability.

Escape routes and safe points in natural hazards. A case study for soil

The scope of this work was to develop soil liquefaction and landslide hazard maps, by computing Peak Ground Acceleration (PGA) for a return period of 475 years, to outline safe points and escape routes designed for schools situated in the drainage basin of Xerias River at northeastern Peloponnese, Greece. The school locations were spatially correlated with each geohazard map to assess the potential hazards to schools within the study area. Web maps illustrating safe and unsafe areas for both geohazards were developed though the GIS online platform for the town of Corinth and four settlements of the study area, where schools exist. Safe points were identified, and routes for evacuation from schools to these locations were mapped using the shortest paths on the existing road network for each geohazard. All the data was integrated into the GIS application available to the public. The findings indicated that 75% of the schools in the study area are situated in areas susceptible to soil liquefaction and landslide hazards. The northern part of the town of Corinth, which includes 62% of the town's schools, is situated in unsafe areas to soil liquefaction. Unsafe areas for landslides are located in the central and northern section of the town, encompassing 35% of the schools. The school buildings in one settlement within the study area are situated in safe areas for both geohazards. The schools in the remaining settlements are sited in areas considered unsafe due to either liquefaction or landslides hazard. A total of 12 safe points were proposed for soil liquefaction hazard and 17 for landslide hazard across all the studied urban areas. The suggested escape routes for both geohazards range from 200 to 1094 m, distances that teachers and students can easily walk. The proposed method can rapidly and effectively identify safe locations and evacuation routes, facilitating authorities in planning for evacuations.

Understanding Multi-Hazard Interactions and Impacts on Small-Island Communities: Insights from the Active Volcano Island of Ternate, Indonesia

Drawing on a case study from Ternate Island, a densely populated volcanic island in Eastern Indonesia, this research illustrates how multi-hazards and extreme weather events are likely to compound and cascade, with serious consequences for sustainable development in small island context. At the heart of Ternate Island sits the active Gamalama volcano, posing a constant eruption threat. Its location within the Ring of Fire further exposes the island to the risks of tsunamis and earthquakes. Additionally, the island’s physical features make it highly susceptible to flooding, landslides, and windstorms. Rapid urbanization has led to significant coastal alterations, increasing exposure to hazards. Ternate’s small-island characteristics include limited resources, few evacuation options, vulnerable infrastructure, and inadequate resilience planning. Combining GIS multi-hazard mapping with a structured survey in 60 villages in Ternate, this case study investigates the multi-hazard exposure faced by the local population and land coverage. The findings suggest significant gaps between village chiefs’ perceptions of the types of hazards and the multi-hazard assessment in each village. Out of 60 village chiefs surveyed, 42 (70%) are aware of earthquake risks, 17 (28%) recognize tsunami threats, and 39 see volcanoes as a danger. GIS assessments show that earthquakes could impact all villages, tsunamis could affect 46 villages (77%), and volcanoes could threaten 39 villages. The hazard map indicates that 32 villages are at risk of flash floods and 37 are at risk of landslides, and extreme weather could affect all villages. Additionally, 42 coastal villages on Ternate Island face potential extreme wave and abrasion disasters, but only 18 chiefs acknowledge extreme weather as a threat. The paper argues that addressing the cognitive biases reflected in the perceptions of community leaders requires transdisciplinary dialogue and engagement.

Risk assessment of disaster chain in multi-seam mining beneath gully topography

Mining-induced geological disasters in gully landforms exhibit distinct characteristics of disaster transformation, with various types of disasters often displaying temporal and spatial chain reactions. The research on the risk assessment of disaster chains in multi-seam mining beneath gully topography is still in its nascent stage. The research area, characterized by its multi-seam mining and gully landforms, has been confronting significant natural and anthropogenic environmental issues within the region. Mining subsidence and mining landslides represent critical disasters in this area. Consequently, this study quantified the degree of disturbance caused by multi-seam mining, considering it as a factor influencing subsidence, which was incorporated into the classification analysis of mining subsidence intensity alongside geological factors and distance from geological structures. A random forest (RF) model was employed to generate a subsidence hazard mapping, and the outcomes of subsidence hazard mapping were utilized as one of the triggering factor parameters for landslide susceptibility mapping (LSM). Simultaneously, a rainfall-runoff layer was introduced to convert the non-uniformity of rainfall, which is challenging to consider in small research areas, into the non-uniformity of runoff, which is used in the LSM together with elevation, slope, aspect, and landcover. The resultant map corresponded with the field investigation results, validating its added value. Moreover, the formation mechanism and risk analysis of the multi-disaster chain, specifically the “subsidence-landslide-barrier lake” in the research area were investigated. The findings indicate that the gully terrain provides a natural advantage for the occurrence of disasters, and rainfall and multi-seam mining exacerbate the vulnerability of the regional geological environment, fostering a complex and protracted disaster chain within the gully area. Lastly, a comprehensive assessment of multi-disaster risks was conducted, and corresponding mitigation measures and recommendations were proposed for various risk stages.

Wildfire Impacts on O3 in the Continental United States Using PM2.5 and a Generalized Additive Model (2018-2023).

We examined PM2.5 and Hazard Mapping System smoke plume satellite data at ∼600 United States (US) air monitoring stations to identify surface smoke on 14.0% of all May-September days for 2018-2023, with large influences in 2020 and 2021, due to California fires, and 2023, due to Canadian fires. Days with smoke have an average of 11 μg m-3 more PM2.5 and 8 ppb higher maximum daily 8 h average (MDA8) O3 concentrations than nonsmoke days, and they also account for 94% of all days that exceed the daily PM2.5 health standard (35 μg m-3) and 36% of all days that exceed the O3 health standard (70 ppb). To estimate the smoke contributions to the O3 MDA8, Generalized Additive Models (GAMs) were built for each site using the nonsmoke day data and up to 8 predictors. The mean and standard deviation of the residuals from the GAMs were 0 ± 6.1 ppb for the nonsmoke day data and 4.3 ± 7.9 ppb for the smoke day data, indicating a significant enhancement in the MDA8 O3 on smoke days. We found positive residuals on 72% of the smoke days and for these days, we calculate an average smoke contribution to the O3 MDA8 of 7.8 ± 6.0 ppb. Over the 6 year period, the percentage of exceedance days due to smoke in the continental US was 25% of all exceedance days, and the highest was in 2023 (38%). In 2023, the Central US experienced an unusually high number of exceedance days, 1522, with 52% of these impacted by smoke, while the Eastern US had fewer exceedance days, 288, with 78% of these impacted by smoke. Our results demonstrate the importance of wildland fires as contributors to exceedances of the health-based national air quality standards for PM2.5 and O3.

The Camp fire and perinatal health: An example of the generalized synthetic control method to identify susceptible windows of exposure.

The November 2018 Camp fire was the most destructive wildfire in California history, but its effects on reproductive health are not known. We linked California birth records from 2017-2019 to daily smoke levels using U.S. EPA Air Quality System (AQS) PM2.5 data and NOAA Hazard Mapping System smoke plume polygons during the Camp fire. In the main analysis, pregnancies were considered exposed if they had median AQS PM2.5 levels above 50 μg/m3 for at least 7 days during November 8-22, 2018. We calculated rates of preterm birth and the infant sex ratio based on week of conception and used the generalized synthetic control method to estimate the average treatment effect on the treated and to propose a novel approach to identify potential critical weeks of exposure during pregnancy. We found associations between Camp fire-related smoke exposure and rates of preterm birth, with a risk difference (RD) = 0.005, 95% CI 0.001, 0.010. Exposure during week 10 of pregnancy was consistently associated with increased preterm birth (RD = 0.030, 95% CI 0.004, 0.056). We did not observe differences in the infant sex ratio. Camp fire smoke exposure was associated with increased rates of preterm birth, with sensitive windows in the first trimester.

Comparative Analysis of Different Forest Fire Susceptibility Models in Benguet, the Philippines

Fire is a common threat to forests in our country. It is usually uncontrolled and consumes the natural fuels of the forest, affecting both the vegetation and the soil. In the Philippines, forest fires are mostly frequent in regions with naturally occurring vegetation that is highly susceptible to forest fire, and one of them is the Cordillera Administrative Region. This study was conducted to [1] determine forest fire occurrences in Benguet province, [2] generate forest fire hazard maps using three indices [structural fire index (SFI), fire risk index (FRI), and hybrid fire index (HFI)], and [3] recommend potential forest management strategies to reduce fire susceptibility or manage fire occurrence in the province. NDMI, elevation, slope, aspect, distance from roads, and vicinity to settlements were the influencing factors used in the occurrence of forest fires. Moreover, forest fire occurrences in the province were collected from DENR records. Results revealed that from 2019–2021, a total of 90 forest fire incidents were observed in the area. In terms of the indices, results showed that maps using HFI and FRI revealed better results than the SFI model. Furthermore, there were forest fire incidences that coincided with the FRI model, particularly in those areas identified as having high and very high susceptibility ratings.

Joint probability analysis and mapping of typhoon-induced wind, wave, and surge hazards along southeast China

The southeastern coastal region of China frequently faces typhoon attacks, posing significant threats to its ever-growing infrastructure. Quantifying multiple typhoon-induced hazards in terms of wind, wave, and surge would provide valuable data to support structural design, risk assessments, and disaster mitigation. This study simulated the evolution of wave and surge driven by typhoon wind fields using the SWAN + ADCIRC coupling model, generating extreme samples of wind speed, significant wave height, and surge height. Trivariate joint probability distributions were established at six typical coastal stations (Hong Kong, Xiamen, Fuzhou, Wenzhou, Zhoushan, and Shanghai) based on optimal copula functions. Using the environmental contour and conditional return period methods, appropriate combinations of design environmental loads were analyzed for a specific return period. The results revealed two reasonable combination types for the 100-year design period. One involves the combination of the 100-year dominant variable value computed using a univariate probability distribution, with two 20-year secondary variable values calculated using the second conditional return period. The second combination selects a variable group corresponding to the maximum value of the dominant variable on the environmental surface. These methods and findings were further extended to the entire computational area to develop hazard maps covering the Southeast Sea area of China. The hazard maps show that the design values of significant wave height increase with distance from the coast. In contrast, the spatial distribution of surge presents the opposite pattern. This study provides a novel perspective on structural design values subjected to multiple disasters and offers a reference for offshore engineering construction in the sea area of southeast China.

A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping

A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping

A MAP-based approach to identify the social vulnerability to flood hazard in states adjacent to Mexican Gulf

The impact of floods can significantly differ based on the local circumstances. The severity of the impact caused by a natural flood in one area compared to another depends on the distinct vulnerability factors present within the affected community, encompassing cultural, social, and economic aspects. Understanding a society's capacity to anticipate, adapt to, endure, and recover from the effects of natural floods relies on evaluating its social vulnerability, which is closely connected with potential risks and community resilience. This study adopts a framework that can analyze the interaction between natural hazards and social factors and is flexible to be applied to different geographic locations. This framework encompasses flood hazards and social vulnerability within the scope of flood risk reduction. This paper explores how social vulnerability can be integrated into the analysis of flood risks within the states adjacent to the Mexican Gulf. Initially, social components are selected and applied to measure and map the Social Vulnerability Index (SVI). Next, a MAP-based methodology is adopted to identify the spatial variability of social vulnerability concerning flood hazards. Then, a risk matrix is developed and the classifications on SVI map are aligned with those on a flood hazard map; provided by the Federal Emergency Management Agency (FEMA). This led to the development of a qualitative social vulnerability exposure map to flood hazards, emphasizing regions marked by high levels of both flood risk and social vulnerability. The findings of this study highlight the importance of integrating social vulnerability into flood risk mitigation policies and emergency management, and planning.

A Short Communication on Flood Hazard Mapping in Japan

A Short Communication on Flood Hazard Mapping in Japan

Risk of Tropical Cyclones and Floods to Power Grids in Southeast and East Asia

Power grids play a critical role in modern society, serving as the lifeline of a well-functioning economy. This article presents a first large-scale study on the risk estimation of tropical cyclone (TC)-induced winds and coastal floods, which can widely impact power grids in Southeast and East Asia. Our comprehensive risk model incorporates detailed infrastructure data from OpenStreetMap (OSM) and government power grid maps, along with global hazard maps and vulnerability curves. The results reveal that the estimated expected annual damages from TCs and coastal floods to OSM-mapped assets account for approximately 0.07% (0.00–0.38%) and 0.02% (0.00–0.02%) of the total GDP of the study area, respectively. We analyzed the main sources of uncertainty in the risk model and emphasized the importance of understanding asset vulnerability. These results highlight the urgent need to strengthen power infrastructure to withstand the impacts of natural hazards, and the significance of reliable risk information for improving power grid design and planning. Focusing on developing more region-specific infrastructure data and vulnerability curves will improve the accuracy of risk estimation and provide valuable insights not only for the electricity sector but also for customers of other infrastructure systems that heavily rely on a stable supply of electricity.

Geotechnical Assessment of Seismic Vulnerability in the Built Environment of Fako Division, South West Region of Cameroon

Introduction: Volcanic earthquakes may trigger severe hazards with considerable impact on the natural and built environment and the surrounding population. Aim: Evaluate the geotechnical characteristics of soils in Fako division to estimate their seismic vulnerability and recommend suitable stabilization techniques to enhance the safety and resilience of built environments. Place and Duration of Study: This study was conducted over a period of 12 months beginning from June 2022 to May 2023. Methodology: It employed a multi-criteria geotechnical approach, involving soil sampling, laboratory analysis, and seismic risk analysis. Soil samples collected from various locations within the study area, were subjected to laboratory tests to determine their plasticity (Atterberg limits) and load-bearing capacity (California Bearing Ratio - CBR). Historical data analysis and seismic hazard mapping were used to assess seismic vulnerability which helped to identify highly susceptible and unstable areas that may require engineered interventions. Results: The findings revealed significant variations in soil plasticity and load-bearing capacity, correlating with observed settlement and instability issues in the region. Historical seismic events, highlighted significant structural damage in areas with high plasticity and low CBR values, underscoring the necessity for robust soil stabilization measures (geotechnical interventions) to mitigate seismic risks and enhance the resilience and safety of structures, reducing the risk of settlement and instability within the Mount Cameroon area, particularly Fako Division.

Derivation of a GIS-Based Flood Hazard Map in Peri-Urban Areas of Greater Lomé, Togo (West Africa)

Derivation of a GIS-Based Flood Hazard Map in Peri-Urban Areas of Greater Lomé, Togo (West Africa)

Enhanced fusiform rust hazard maps for loblolly pine: Incorporating genotype and climate to predict disease

Hazard maps are a critical component in determining the regional disease pressure for fusiform rust disease [caused by the fungal pathogen Cronartium quercuum (Berk.) Miyabe ex Shirai f. sp. fusiforme], which is considered the most important disease of loblolly pine (Pinus taeda L.) in the southeastern United States. Genetically improved fusiform rust resistant stock should be deployed in areas that are estimated to be at a high hazard for the disease, while stocks with lower resistance to the disease, but perhaps other favorable traits, can be deployed in areas with a lower estimated hazard. Current hazard maps are based on the historic presence of the disease but do not consider the effect of host genotype or climate which can influence the presence and distribution of fungal pathogens. Here, we aim to improve current hazard maps for fusiform rust by identifying, modeling, and predicting the relationship between the disease and yearly climate indicators. Different loblolly pine genotypes were tested: one family improved for resistance and non-improved checklots for each location. We found that, for the non-improved genotypes, location, potential evapotranspiration, consecutive cloud cover, diurnal temperature range, and a yearly available water indicator can explain 89.2 % of the deviance for disease occurrence. For the improved family, we found that longitude, yearly water deficit, yearly consecutive frost days, yearly consecutive cloud cover, and yearly diurnal temperature range explain 93.1 % deviance for disease incidence. We produce two rust hazard maps based on each model, illustrating the probability of the incidence of the disease. With this project, we pave the way for a better-informed determination of where and what loblolly pine resistant stock should be deployed to limit the damage and costs associated with the management of fusiform rust disease.

ОБЗОР СЕЛЕВОЙ ОПАСНОСТИ ДОЖДЕВОГО ГЕНЕЗИСА ПО ГОРНОЙ ТЕРРИТОРИИ РЕСПУБЛИКИ КАЗАХСТАН ЗА 2023 ГОД

The review was prepared based on the results of monitoring the debris-flow hazard caused by rainfall in the mountainous territory of the Republic of Kazakhstan for 2023. Monitoring was carried out for debris-flow hazard areas of Almaty, Zhetysu, Zhambyl, Turkestan, Mangistau, Abay and East Kazakhstan regions. The Ile, Kungey, Teriskey, Zhetysu, Kyrgyz and Talas Alatau, Ugam, Mangystau, Saur, Tarbagatai and Kazakhstan Altai mountains ridges are debris-flow hazard areas. The Review contains a map of debris-flow hazard on the territory of the Republic of Kazakhstan, information on the hydrometeorological situation and debris f low occurrences during the 2023 debris-flow-hazardous period. In the monitoring process information from the state observation network of the RSE «Kazhydromet» in operational mode, synoptic and numerical weather forecasts of the RSE «Kazhydromet» was used, etc.

Observation and modelling of infragravity waves at a large meso-tidal inlet and lagoon

The role of infragravity waves (IG waves) in beach and dune erosion or in flood hazard has been extensively studied on open beaches. In contrast, the detailed characterization of IG waves and their contribution to the Total Water Level (TWL) along the shore of inlets received little attention so far. In such environment, there is a real lack of in situ observations of waves and hydrodynamics conditions at appropriate spatial and temporal coverage to study the role of infragravity (IG) waves (long waves of frequency typically ranging between 0.004 Hz and 0.04–0.05 Hz) on coastal hazards. This contribution is based on field observations collected at the Arcachon Lagoon, a shallow semi-enclosed lagoon connected to the ocean by a large tidal inlet, located in southwest France. Analyses combine observations made at several locations during storm events within the inlet and the lagoon with numerical simulation with the XBeach surfbeat model to explore the spatial variability of IG waves and simulate observed, historical, and idealized storm conditions. The results show that IG waves are substantial during typical winter storms at the inlet and range from Hm0 = 0.8 to over 1 m across the ebb delta and about 0.4–0.6 m in the inner part of the inlet. At the lagoon entrance, IG waves remain substantial (about 0.1–0.2 m) and decrease to a few centimeters at the lagoon shore. The spatial variability and magnitude of IG waves along the inlet coast, simulated for the historical storms, are quite comparable to those observed during classical winter, and do not increase linearly with offshore wave energy. However, both observations and simulations reveal local amplifications of IG waves in the inner part of the inlet, especially along the sheltered coast were IG waves dominate the variance of free surface elevation, reaching about 0.6–0.7 m during common storms and more than 1 m for an extreme storm scenario. A numerical experiment indicates that IG wave reflection from one coast to the other contributes up to 35–40% of the measured IG wave height at a hot spot located along the sheltered coast. Finally, the contribution of IG waves to TWL at the shore on both sides of the inlet has been estimated to be about 0.4–0.6 m for a common storm and 0.6–0.9 m for an extreme scenario, locally peaking at 0.74 and 1.1 m respectively and overpassing the contribution of wave-induced setup. This work provides new insights into the contribution of IG waves to TWL and its implications for overtopping flooding hazard and overwash processes at large inlets, highlighting the need to consider IG waves in Early Warning Systems or hazard mapping for flood prevention plans in these environments.

Modelación de mapa de inundaciones durante el Huracán Alex: simulación usando precipitación multisensorial en la ciudad de Monterrey, México

Alex Hurricane was one of the most intense tropical cyclones in the North Atlantic, that caused fatalities and loses in the Northeast of Mexico due to the flash floods. Flood hazard mapping is a vital tool to assess inundation areas, which can be simulated using hydraulic and hydrologic models. This study describes the modelling of a flood event during Alex Hurricane in the Santa Catarina River Watershed, Northeast of Mexico, applying HEC-HMS and two dimensional (2D) HEC-RAS models forced with Multi Radar Multi Sensor - Quantitative Precipitation Estimation (MRMS-QPE). A HEC-HMS model was developed forced by (MRMS-QPE) as input to simulate discharges along the Santa Catarina River. The simulated discharges were introduced as border conditions along the mainstream of the Santa Catarina River inside a HEC-RAS 2D model to simulate a flood map along the mainstream of the Santa Catrina River. The observed against the simulated peak discharges achieved a R-squared of 0.97 and a Nash - Sutcliffe coefficient of 0.97. The observed against the simulated accumulated discharges achieved a R-squared of 0.99 and a Nash - Sutcliffe coefficient of 1.0. The observed against the simulated stages achieved a R-squared of 0.74 and, a Nash - Sutcliffe coefficient of 0.68. The use of HEC-HMS and HEC-RAS 2D models coupled with MRMS-QPE. shows that these models are user friendly to setup, the model has stability and the capacity to simulate flood maps along the whole mainstream of the Santa Catarina River with good results.

Evaluating Coseismic Landslide Susceptibility Following the 2022 Luding Earthquake: A Comparative Analysis of Six Displacement Regression Models Integrating Epicentral and Seismogenic Fault Distances within the Permanent-Displacement Framework

Coseismic landslides have the potential to cause catastrophic disasters. Thus, it is of crucial importance to conduct a comprehensive regional assessment of susceptibility to coseismic landslides. This study rigorously interprets 13,759 coseismic landslides triggered by the 2022 Luding earthquake within the seismic zone. Employing the Newmark method, we systematically assess the susceptibility to coseismic landslides through the application of six distinct displacement regression models. The efficacy of these models is validated against the actual landslide inventory using the area under the receiver operating characteristic (ROC) curve. A hazard map of coseismic landslides is generated based on the displacement regression model with the highest degree of fit. The results show that Moxi Town, Detuo Town, the flanks of the Daduhe River, Wandonghe River, Hailuogou River, and Yanzigou River are high-susceptibility areas for coseismic landslides. This study explores factors influencing model fit, revealing that the inclusion of the epicentral distance and the distance to the seismogenic fault in displacement prediction enhances model performance. Nevertheless, in close proximity to fault zones, the distance to the seismogenic fault exerts a more significant influence on the spatial distribution density of coseismic landslides compared to the epicentral distance. Conversely, in regions situated further from fault zones, the epicentral distance has a greater impact on the spatial distribution density of coseismic landslides compared to the distance to the seismogenic fault. These findings contribute to a nuanced understanding of coseismic landslide susceptibility and offer valuable insights for future Newmark method-based coseismic landslide displacement calculations.

Deterministic approach to assess landslide susceptibility and landslide activity in the Central-Western Region of Slovakia

Quantitative assessments in landslide susceptibility evaluation are typically dominated by statistical analyses. However, this paper employs a deterministic approach to construct such maps, specifically focusing on the stability assessment of shallow landslides with slip surfaces generally not exceeding 5 m in depth at a regional scale. The study area chosen for this research is the landslide-prone region between Hlohovec and Sereď towns in the central-western part of Slovakia. Our focus is on the activation of shallow landslides, representing the youngest, third generation of landslide evolution in the area. For the stability assessment, three scenarios were considered using deterministic analysis: a scenario involving dry slopes, a scenario under partially saturated conditions, and a scenario of fully saturated slopes. This approach enables the assessment of stability conditions based on qualitative parameters derived from both field and laboratory research. The results from the deterministic analysis aid in defining different stages of activity within the landslide body. These Landslide susceptibility maps were verified directly in the field, and most landslides reactivated from 2011 to the present fell into the class with the safety factor below 1.0. In comparison with the landslide hazard map created by using bivariate statistical analysis, it was observed that 89.97% of the area is in a very high level of landslide hazard, given a safety factor below 1.0. This highlights the effectiveness of the deterministic approach in capturing the vulnerability of the region to shallow landslides.