Natural hazards in mountainous regions

Abstract/Description

The Himalayas are prone to several geo-hydrological disasters such as flash floods, debris flows, landslides, mass movements, and rock falls. The Cloudbursts and glacier bursts usually trigger these disasters that are very active and frequent in the Himalayan regions and happens mainly during the peak monsoon season usually from July to September. The study assesses the geo-hydrological disasters of the Uttarakhand Himalaya that occurred from July to September, 2022. Additionally, the study examines the magnitude of the disaster and maps the disaster hotspot areas in the Uttarakhand regions. The study employs both qualitative and quantitative approaches. The Data on geo-hydrological disasters which occurred during the monsoon season were collected from the Uttarakhand State Disaster Management Authority. Besides, an empirical study of the Song and Bandal and River valleys was carried out. These two river valleys were severely hit by cloudburst-triggered debris flows and flash floods in 2022. A household-level survey of the damage caused by huge flash floods and debris flows in three villages of Bandal valley was carried out. The data was analyzed and mapped and the Uttarakhand Himalayas was divided into various disaster hotspot zones. The study suggests that the construction of settlements must be banned in the vulnerable areas, along the river valleys, and on the fragile slopes. Nature-based disaster risk reduction approach should be implemented to reduce the impact of geo-hydrological disasters in the Himalayan regions.

Abstract/Description

Forest fires in mountainous regions, particularly in the Indian Himalayan Region, are becoming an increasingly inevitable and destructive phenomenon. Over recent years, the frequency, size, and intensity of these fires have grown significantly, exacerbating the challenges faced in fire management. Despite the increased financial investments and infrastructure support, existing fire management strategies at both state and central levels have remained largely ineffective. This study emphasizes the need for restructuring policies, such as the National Action Plan for Forest Fires and the Forest Fire Prevention and Management Scheme, to develop more science-based and practical approaches. The analysis of fire incidents, using near-real-time data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and fire spot information from NASA’s Fire Information Management System, will inform the policy review and highlight the necessity for a comprehensive, forest-type inclusive fire management framework. Building upon this context, the study further investigates the spatiotemporal patterns and predictive modeling of forest fires in Uttarakhand using a dataset from the Global Fire Atlas (2003–2016). This dataset includes key fire attributes such as fire start and end dates, duration, and burned area, alongside environmental variables derived from Google Earth Engine (GEE). These variables encompass elevation, slope, aspect, temperature trends, NDVI, precipitation, and proximity to roads. Through data preprocessing, including handling missing values and encoding categorical variables, and spatial autocorrelation analysis using Moran’s I and z-scores, the study identifies weak but statistically significant spatial clustering of fire duration and size. A range of machine learning (ML) and deep learning models, including Random Forest Regression, LightGBM, XGBoost, CatBoost, LSTM networks, and ARIMA, were used to predict fire duration. Model performance was evaluated using mean absolute error (MAE) and root mean squared error (RMSE), revealing the varying predictive capabilities of these algorithms. The results of this study provide valuable insights for optimizing fire management strategies, offering a basis for better resource allocation, risk mitigation, and environmental protection.

Abstract/Description

Rockfalls are one of the main natural hazards that cause significant damage and loss of life globally, especially in mountainous regions. These hazards are particularly dangerous due to their unpredictability and varying magnitudes. Rapid urbanization and inadequate infrastructure in developing countries increase vulnerability to these risks. This study aimed to investigate the stability of rock slopes in the northern part of Gebel El-Mokattam in East Cairo, Egypt. This work includes intensive field and laboratory tests to collect geological, structural, geomorphological, and geotechnical data from this region. This area has been affected frequently by rockfalls and landslides that impacted both the inhabitants and infrastructure. Field investigations revealed that the rock slopes are primarily composed of highly fractured limestone with medium to weak strength interbedded with highly swelling clays. During this research, rockfall modeling was conducted through trajectographic analysis on representative slope profiles to simulate the path of falling blocks. This analysis provided valuable data on the kinetic energy, bounce height, and runout distance of falling blocks, helping to evaluate the risk to nearby buildings and infrastructure. Furthermore, a rockfall rating system has been applied to evaluate the magnitude of the risk. The results show a high risk of rockfalls. Therefore, several preliminary and major mitigation strategies were recommended and tested to reduce rockfall hazards, which are directly linked to the results of rockfall modeling. This study highlights the importance of rockfall modeling in understanding hazards in mountainous areas. Also, the findings of this research will assist land-use planners and decision-makers in selecting appropriate mitigation strategies in areas of complex geomorphological and geological factors.

Abstract/Description

Mountain soils are crucial for environmental sustainability and climate change mitigation. However, invasive species like bracken fern (Pteridium aquilinum) threaten these ecosystems. P. aquilinum, found globally except Antarctica, disrupts soil processes and biodiversity. In Malawi, particularly on Mulanje Mountain and Nyika plateau, it has become a dominant invasive species, significantly displacing grasslands and posing a substantial ecological threat. This study investigated the impact of physical and chemical control methods on soil chemistry in grasslands of Mulanje Mountain and Nyika National Park, Malawi, heavily invaded by the bracken fern Pteridium aquilinum. The research, conducted over four years, examined the effects of chemical treatments (Forester, Eco-Imazypyr, and lime application) and physical methods (mowing and slashing/cutting) on soil pH, soil organic carbon (SOC) stock, exchangeable calcium (Ca), magnesium (Mg), available phosphorus (P), total nitrogen (N), and the Carbon-to-Nitrogen (C:N) ratio. Four experimental plots (35m x 29m) were established at each site, with treatments randomly assigned and replicated. At least six soil samples were randomly collected from each of 5m x 5m quadrants at a depth of 0.15m before and after treatment application and analyzed using standard soil analysis procedures. Data were analyzed using ANOVA (Minitab 18.0). The results revealed significant differences (P<0.05) in total nitrogen levels, soil pH and organic carbon stock levels, with lime treatment resulting in the highest (14.46%) increase in pH from the initial 4.08 to 4.67 and Forester treatment yielding the most substantial (64.4%) increase in SOC from the initial 58.14Mg C/ha to 95.62Mg C/ha. The Forester treatment led to the most substantial (64.4%) increase in SOC, from 58.14 Mg C/ha to 95.62 Mg C/ha. No significant differences were observed in exchangeable calcium, magnesium, or C:N ratios. However, all treatments significantly enhanced available phosphorus in the soil, with physical methods, particularly mowing, showing notable effects. In conclusion, both physical and chemical control methods for P. aquilinum positively influenced soil chemistry. The increases in soil pH and organic carbon suggest improved soil health. These findings highlight the importance of implementing effective management strategies to preserve mountain soils and maintain ecological balance in the face of invasive species.

Abstract/Description

Acacia mearnsii (Black Wattle), native to Australia, has become an invasive species in regions like Africa, including Malawi, causing ecological disruptions. Its aggressive growth threatens native biodiversity by outcompeting indigenous plant species and disrupting agricultural practices. Various control methods have been implemented, but their impact on soil’s physiochemical properties remains understudied. This study examines the impact of Acacia mearnsii control methods (uprooting and Impala chemical application) on soil properties in Nyika National Park over four years. Two experimental plots (29 m x 23 m) were established, and treatments were replicated at least five times in 5 m x 5 m quadrants. Soil samples were collected at a depth of 0.15 m before and after treatments and analyzed for pH, soil organic carbon (SOC), total nitrogen (N), exchangeable calcium (Ca²⁺) and magnesium (Mg²⁺), available phosphorus (P), exchangeable sodium percentage (ESP), and electrical conductivity (EC). Data analysis was conducted using One Way Analysis of Variance (ANOVA) via Minitab 18.0 to assess the effects of the treatments on soil properties. The findings showed significant differences (P < 0.05) between treatments in exchangeable magnesium and calcium levels, total nitrogen content, and electrical conductivity. Notably, Impala treatment increased SOC by 30.4% and exchangeable calcium by 81.7%, while uprooting increased SOC by 8.1% and calcium by 60.3%. Impala chemical treatment significantly enhances soil organic carbon (SOC) and exchangeable calcium levels more effectively than uprooting, indicating that chemical applications can provide a quicker restoration of vital soil nutrients. Conversely, uprooting demonstrated a substantial increase in available phosphorus, increasing it by 73.1%, suggesting its potential benefits for long-term nutrient availability, though Impala treatment resulted in a more pronounced increase of 90.5%. On the other side, uprooting decreased both exchangeable sodium levels and electrical conductivity, but Impala treatment led to significant increases in these parameters. Both treatments led to a reduction in total nitrogen levels, decreasing from an initial 2.52% to a final value of 0.61% for uprooting, and from 2.51% to 0.62% for Impala treatment. Therefore, both methods modify soil properties, offering insights for ecological restoration, with chemical interventions yielding quicker results and uprooting supporting sustained nutrient enhancement.

Abstract/Description

Debris flows are a significant geohazard in mountainous regions, posing substantial risks to critical infrastructure, including water, oil, and gas pipelines. Understanding and predicting the impact forces exerted by debris flows on pipelines is crucial for enhancing the resilience and sustainability of these infrastructures. This study investigates the relationship between the rheological properties of debris flows such as shear stress, shear strain, viscosity, and normal stress and the resulting impact forces on pipelines. The significance of this study lies in its potential to improve the design and protection of pipeline infrastructure, thereby reducing the risk of catastrophic failures and ensuring the safe and efficient operation of critical energy systems. The study utilizes a comprehensive dataset obtained from experimental measurements of debris flow samples with varying volume fractions (S0 to S7). Each sample was characterized by its rheological properties, including shear stress, shear strain, viscosity, and normal stress. These properties were measured using a digital hybrid rheometer. Utilizing experimental data from seven samples with varying volume fractions, various machine learning techniques (ML) were employed to predict impact forces. The ML techniques include Random Forest and Gradient Boosting Machines (GBM) models which are developed and validated using 5-fold cross-validation, demonstrating high predictive accuracy. The analysis revealed that rheological properties significantly influence the magnitude and distribution of impact forces on pipelines. The Random Forest model demonstrated high predictive accuracy, with an RMSE of 0.12 and an R² of 0.95, indicating strong model performance. The GBM model also showed robust predictive capabilities, with an RMSE of 0.15 and an R² of 0.93. These findings underscore the necessity for incorporating rheological properties into the design and protection of pipeline infrastructure. By integrating experimental data with advanced predictive modeling, this research provides practical guidance for enhancing the resilience and longevity of pipeline systems in geohazard-prone areas.

Abstract/Description

Nepal is situated in the seismically active Himalayan region, where the construction of roads has notably increased the frequency of slope instabilities. These instabilities, including landslides, rockfalls, topple events, debris flows, and gully erosion, are commonly observed along roads in the Nepal Himalayas. This research investigates the causes and mechanisms behind landslides along a section of the Beni-Jomsom Road in Gandaki Province, Nepal. The study applies time series analysis to examine the mechanisms and frequencies of different instability types, taking into account various environmental factors along the road. The research employs specific tools GIS, WAVELET, SPSS, ORIGIN to analyze the landform, surrounding geology, geological structures, and geotechnical characteristics of the soil and rocks. The study reveals that the primary causes of instability along the road are translational slides, debris flows, and rockfalls/rockslides, with these events occurring repeatedly after road construction. The results highlight a clear connection between road-building activities and the occurrence of slope instability in the region. This research will serve as an important reference for authorities, providing valuable insights for managing and mitigating slope instability in future road construction projects.

Key Words: Hill Roads, Slope instabilities, Mechanism, Time series analysis, Himalaya.

Abstract/Description

Mountain regions necessitate efficient yet sustainable protection measures to face natural hazards. Gabions offer a convenient, environmental friendly solution to face problems ranging from soil erosion control, river bank stabilization, stream energy dissipation, landslides prevention, creation of rockfall safety barriers, and protection of structures like bridge abutments or viaduct foundation. Filling gabion baskets with natural resources like ad hoc quarried rock fragments often turns to be expensive and poses environmental concerns. C&DW offer an alternative solution for creating more sustainable structures, provided their engineering performance is proved together with their environmental compatibility. A comprehensive research has been undertaken in the geotechnical laboratory of the University of Cassino and Southern Lazio to investigate the mechanical and environmental performance of gabions filled with different C&DW. The mechanical performance is detected by performing unconfined and confined uniaxial compression tests using an ad-hoc designed equipment on smaller scale models (0.4×0.4×0.4 m) of gabions filled with different blocks. They include concrete fragments artificially created with variable types and proportions of components (cement, water and aggregates) to infer controlled properties to the constituent material. Their performance is compared with that of gabions filled with natural rock fragments, the latter used for reference. In all cases, the mechanical properties of the constituent materials are evaluated with specific tests (point load, uniaxial compression, Los Angeles abrasion and freeze-thaw) to characterize material strength and fix standard acceptance levels. In a second phase, focus is given to the combination of high-quality materials (recycled concrete) with lower-quality components (bricks) to optimize performance and resource utilization. Stiffness and strength of gabions are examined to determine their influence on the overall performance of structures distinguishing the effects of particle sizes, shapes, and material combinations on deformation behaviour and load-bearing capacity. This study contributes to the sustainability of mountain hazard mitigation measures, addressing the increasing need for high performance materials with circular economy solutions based on the transformation of wastes into valuable resources. Its outcomes aim to establish guidelines for the practical implementation of C&DW in gabion structures.