Steep Terrain Research Articles

Effect of particle gradation and rock block content on soil-rock mixture shear strength parameters

Soil-rock mixture (SRM) is highly inhomogeneous and loose geomaterial found in the quaternary formation, composed of a certain percentage of rock blocks, fine-grained soil, and pores. In Peninsular Malaysia, SRM can commonly be found in the granitic formation covering the mountainous areas where slope failures are prone to occur due to deep tropical weathering, heavy rainfall, and steep terrain. Numerous research studies have been conducted to investigate the mechanical behaviour of SRM, especially the influence of rock block content. However, the effect of particle size distribution has been studied in limited detail. This study uses sieve analysis, specific gravity, compaction, and direct shear box tests to determine the shear strength parameters of weathered type SRM with particle size distribution. The sample collected was divided into well-graded, poorly-graded, and gap-graded types of particle size distribution with 30%, 50%, and 70% rock block content, respectively. The percentage of rock block content is crucial in controlling the shear strength parameters. The specific gravity, dry density, optimum moisture content, and estimated porosity values measured show a considerable difference when the particle size distribution (PSD) and rock block content change. The findings also show that for a well-graded SRM, cohesion and friction angle values show a non-linear relationship against the rock block content. Meanwhile, as the rock block content increases, the cohesion decreases and inversely has an increment of friction angle for poorly-graded SRM. In contrast, the cohesion of gap-graded SRM increases when the rock block content increases. Hence, by understanding the influence of particle size distribution on the shear strength parameters of SRM, a better evaluation can be made in designing slope hazard mitigation.

Assessment of Groundwater Quality and Vulnerability in the Nakivale Sub-Catchment of the Transboundary Lake Victoria Basin, Uganda

This study evaluates the quality and vulnerability of groundwater within the Nakivale Sub-catchment of the transboundary Lake Victoria Basin in Southwestern Uganda. Groundwater quality assessment focuses on its suitability for both drinking and agricultural uses. Hydrochemical analysis of 19 groundwater samples revealed that 90% comply with World Health Organization drinking water standards, although localized contamination was noted, particularly in terms of total iron, nitrate, potassium, magnesium, and sulfates. The drinking groundwater quality index shows that over 90% of the samples fall within the good-to-excellent quality categories. Elevated nitrate levels and chloride–bromide ratios indicate human impacts, likely due to agricultural runoff and wastewater disposal. For irrigation, Sodium Adsorption Ratio analysis revealed medium-to-high salinity hazards in the region, while Sodium Percentage and other parameters indicated low-to-moderate risks of soil degradation. DRASTIC vulnerability assessments identified low contamination risks due to impermeable geological layers, steep terrain, slow groundwater recharge, deep aquifer depth, and clayey soil cover. These findings emphasize the need for conjunctive water resource management, including improved groundwater quality monitoring, public education on sustainable practices, and protective measures for recharge zones and areas highly susceptible to contamination. By addressing these issues, this study aims to preserve groundwater resources for domestic and agricultural use, ensuring long-term sustainability in the region.

Lessons from national approaches: a long uphill struggle in search of sites for repositories for nuclear waste locations

Abstract. A quarter of a century ago, a long-term expert in the nuclear waste scene stated that “the management of spent nuclear fuel and high-level nuclear waste has the deserved reputation as one of the most intractable policy issues facing the United States and other nations using nuclear reactors for electric power generation” (North, 1999, p. 751). Apart of exceptions, this statement is still true. At some points, however, there is light at the end of the tunnel if we can read the signs of the times. It will be a long hike, in steep terrain, poor visibility and with an approximate destination. We need a safe and acceptable site, tolerated by the affected parties, where a repository can be built, operated and, finally, closed down in reasonable course by a generation to come and with a clear conscience. This contribution does not present the silver bullet (which does not exist) but suggests some criteria and characteristics which have not been respected in the history of final disposal – but they should be. It needs adequate resources: stable structures, competent institutions, learning personnel (in institutions and civil society), mature and open discourse as well as sufficient time. Based on https://doi.org/10.5194/egusphere-egu24-6514 (Flüeler, 2024a).

Species behavioral characteristics lead to unique fragmentation threats: The Nubian ibex as a case study

AbstractThe behavioral characteristics of species may result in certain populations being inherently more susceptible to fragmentation. For example, species exhibiting spatial sexual segregation or those constrained to elongated and narrow habitats. We studied the fragmentation threats, spatial dynamics, resource utilization, and movement ecology of a particularly vulnerable species that is both sexually segregated and constrained to elongated and narrow habitat—the north Judean Desert population of Nubian ibex (Capra nubiana). From 2016‒2020 we tracked 48 marked ibex (27 male, 21 female), of which 38 (20 male, 18 female) also had global position system (GPS) collars. Using GPS‐collar and camera‐trap data in zones delineated around perennial water sources (PWSs), we calculated ibex drinking frequencies and individual utilization distributions by season and sex, focusing on their overall (95% isopleth) and core (50% isopleth) home ranges. We quantified joint space use between sexes using a utilization distribution overlap index (UDOI) and ibex daily movements and space use via movement indices. Female groups formed philopatric activity centers that were anchored around PWSs year‐round and arranged in a metapopulation‐like structure, with no female movement detected between them. Conversely, movement of adult males changed seasonally, with the cores of male groups anchored around PWSs only during the dry season, and long‐range movement between female activity centers during the rut. Female groups also spent more time at steeper terrain and higher elevations compared with male groups. Outside the rut, groups of males and groups of females exhibited minimal joint space use (i.e., average dry season UDOI was 0.06). These patterns indicate high sensitivity of this population to intersexual fragmentation by obstacles (physical or virtual). Management strategies to mitigate fragmentation threats for such populations should be sex‐specific and landscape‐oriented.

Framework for urban flood risk assessment – a hydrological modelling approach: the case of Nilambur, Kerala

ABSTRACT The study has evolved in response to the challenges posed by climate change and rapid urbanization occurring worldwide, which contribute to an increased frequency of natural disasters. It addresses flood hazards in Kerala’s Nilambur urban area, where climate change and rapid urbanization heighten disaster risk. Kerala's coastal location and steep terrain in the Western Ghats increase vulnerability, particularly as one of India’s most densely populated states. The research identifies flood risk zones applying overlay analysis, using data from the 2018 Kerala Floods, to create a flood risk framework responsive to runoff volume and thereby flood susceptibility. Key factors include climatic variables, terrain characteristics like elevation, slope, soil, land-use land-cover including urban density, and hydrological and ecological elements like stream networks and floodplains. These are integrated in the SWAT tool to model flood risks, creating a resilient flood risk assessment framework that combines hydrological and ecological parameters.

Characterization of non-Gaussianity in the snow distributions of various landscapes

Abstract. Seasonal snowpack is an important predictor of the water resources available in the following spring and early-summer melt season. Total basin snow water equivalent (SWE) estimation usually requires a form of statistical analysis that is implicitly built upon the Gaussian framework. However, it is important to characterize the non-Gaussian properties of snow distribution for accurate large-scale SWE estimation based on remotely sensed or sparse ground-based observations. This study quantified non-Gaussianity using sample negentropy; the Kullback–Leibler divergence from the Gaussian distribution for field-observed snow depth data from the North Slope, Alaska; and three representative SWE distributions in the western USA from the Airborne Snow Observatory (ASO). Snowdrifts around lakeshore cliffs and deep gullies can bring moderate non-Gaussianity in the open, lowland tundra of North Slope, Alaska, while the ASO dataset suggests that subalpine forests may effectively suppress the non-Gaussianity of snow distribution. Thus, non-Gaussianity is found in areas with partial snow cover and wind-induced snowdrifts around topographic breaks on slopes and on other steep terrain features. The snowpacks may be considered weakly Gaussian in coastal regions with open tundra in Alaska and alpine and subalpine terrains in the western USA if the land is completely covered by snow. The wind-induced snowdrift effect can potentially be partitioned from the observed snow spatial distribution guided by its Gaussianity.

The Multifaceted Soils of Himachal Pradesh, India: Types and Characteristics

The soils of Himachal Pradesh face a multitude of pressing issues that threaten their health and agricultural productivity. The diverse geographical features, temperature regimes and biological zones of Himachal Pradesh have resulted into a vast range of soil types in the state. Alpine, forest, alluvial and sub-mountain soils are the four major soil categories into which the state's soils can be generally divided. Only a particular species and adapted plants can thrive in the extreme weather circumstances of alpine soils, which are located at higher elevations and possess soils having thin, stony and acidic in reaction. The mid-hill areas are dominated by forest soils which are distinguished by their high levels of organic matter and nutrients. These soils support luxuriant vegetation and make effective agricultural methods possible. Alluvial soils, which are mostly found in river valleys and are deposited by ravine systems are extremely rich and perfect in fertility and suitable for growing a range of crops including vegetables, rice and maize. Located in the foothills, sub-mountain soil exhibits a blend of traits from alluvial and forest soils, thus providing a range of uses for horticulture and agriculture. In addition to supporting a diverse array of plant and animal life, Himachal Pradesh's varied soil types are vital to the region's agricultural economy. One of the biggest problems is soil erosion, which is made worse by the steep terrain and abundant rainfall in the area. This results in the loss of topsoil and decreased fertility. Deforestation, overgrazing, and unsustainable farming methods all contribute to land degradation, which further reduces biodiversity and soil quality. Furthermore, crop development is impacted by soil acidity in high-altitude regions, which restricts nutrient availability. However, these issues need efficient conservation measures and soil management techniques. In order to maintain Himachal Pradesh's rich biodiversity and agricultural potential for future generations, sustainable agricultural development and environmental protection in this ecologically sensitive area depend on an understanding of the complex soil dynamics. Addressing these interconnected issues requires a comprehensive approach, emphasizing sustainable land management practices, community engagement, and restoration efforts to preserve the vital soil resources of this ecologically sensitive region.

Effect of Pipe Roughness on Optimal Sewer Networks with Drop Manholes in Steep Terrains

Effect of Pipe Roughness on Optimal Sewer Networks with Drop Manholes in Steep Terrains

Electrical Resistivity Tomography and Boreholes Data to Investigate the Near-Surface Structure under the Campus Area of Çanakkale Onsekiz Mart University, Çanakkale, Turkey (Türkiye)

We study the soil foundation underneath the Çanakkale Onsekiz Mart University (ÇOMU) campus, Çanakkale, Türkiye by employing the electrical resistivity tomography – ERT supported by 27 boreholes data. The studied area taking place in southwest Marmara region was historically affected by large earthquakes () created by the North Anatolian Fault system. The boreholes data show that the near surface structure beneath the ÇOMU campus is made of mostly silty sands and marls. A high sensitivity resistivity instrument is used to collect the field data in which nine ERT profiles reaching lengths as long as 315 m are utilized. The current geoelectrical measurements are simulated by using two numerical models to estimate the inversion depth sensitivity from which it is found satisfactory in the depth range 0-30 m and then somewhat decreasing. The observed electrical resistivity values are in the range 2-160 W m. The geoelectrical structure corresponding to the silty sands are represented by low resistivities (<20 W m) while the high resistivity (>40 W m) depth sections are associated with the marl units. The resistivity structure beneath the ÇOMU campus is complex where both low and high resistivity depth sections reside side by side. The groundwater and clay mineralogy contribute to the broad changes in the subsurface resistivities. The groundwater flow below the steep terrain of the ÇOMU campus causes low resistivities (<10 W m) deeper than 10-m depth. The boreholes data superimposed on the two-dimensional (2-D) ERT profiles show consistency with the resistivity-depth distributions at corresponding depths.

An extreme precipitation event over Dronning Maud Land, East Antarctica - A case study of an atmospheric river event using the Polar WRF Model

Extreme precipitation events (EPEs) are crucial in Antarctica, impacting the Antarctic ice sheet's surface mass balance and stability. Comprehensive case studies are essential for better understanding these events and the underlying processes driving them. Here, we investigate an extreme snowfall event in Dronning Maud Land (DML), East Antarctica on November 8 and 9, 2015. This event contributed approximately 22 % of the annual accumulation in less than two days and exhibited high spatial variability in precipitation distribution. We employed a high-resolution atmospheric model specifically optimized for the polar regions (Polar WRF) and ERA5 reanalysis data to analyze the event in detail. Our findings highlight the importance of a blocking high-pressure ridge of record strength that effectively blocked and diverted a strong extratropical cyclone into DML, ultimately leading to the heavy snowfall event. The sudden deepening of the cyclone was initiated by a ‘jet streak’ in the upper atmosphere that steered the system southeastwards towards the Antarctic coast. Notably, we observed an anomalously high poleward moisture transport in the form of a strong atmospheric river on November 7, 2015. This atmospheric river originated in the South Atlantic Ocean and tracked poleward from the 30°S-40°S latitude band. Vertical cross-sections of the model outputs indicate that most of the precipitation was concentrated in regions with steep orography along the path of the atmospheric river. This interaction between the atmospheric river and the steep terrain led to the uplift of maritime air, resulting in heavy snowfall. This study highlights the significance of extreme upper and lower atmospheric conditions in driving intense moisture transport towards coastal DML. The interaction between the atmospheric river and the steep orography contributed to heavy snowfall, underscoring the importance of considering orographic influences in understanding EPEs in Antarctica.

Machine learning approaches for mapping and predicting landslide-prone areas in São Sebastião (Southeast Brazil)

This study employs machine learning techniques to map and predict landslide-prone areas in São Sebastião, Brazil, a region susceptible to landslides due to its steep terrain and intense rainfall. We compared five algorithms: Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbors, using various environmental factors as inputs. The Gradient Boosting model performed best, achieving an AUC-ROC of 0.963 and an accuracy of 99.6%. Slope degree, soil moisture index, and relief dissection emerged as the most influential factors in predicting landslide susceptibility. Analysis of land use and land cover changes between 1985 and 2021 revealed significant increases in forest cover and urban areas, with implications for landslide risk distribution. The resulting susceptibility map shows predominantly low-risk areas with scattered high-risk zones, providing crucial information for targeted risk management. This research demonstrates the effectiveness of machine learning in landslide susceptibility mapping and offers valuable insights for disaster risk reduction and urban planning in coastal mountainous regions.

Reconstruction of the Hydro‐Thermal Behavior of Regulated River Networks of the Columbia River Basin Using Satellite Remote Sensing and Data‐Driven Techniques

AbstractThe use of satellite‐based thermal infrared remote sensing has facilitated the assessment of surface water temperature on a large scale. However, the inherent limitations of this remote sensing technique make it difficult to assess rivers unless ambient conditions are cloud‐free, devoid of steep terrain and the rivers are at least 60 m wide. To address these challenges that limit the spatiotemporal continuity of satellite‐based hydro‐thermal data, we harnessed the extensive coverage from the Landsat missions' thermal infrared sensors and data‐driven techniques to estimate surface water temperature of rivers. Out of the tested data‐driven techniques, we selected the Random Forest Regressor as our prime non‐linear approach for estimation of surface water temperature in rivers. Using the selected technique, proposed as THORR (Thermal History of Regulated Rivers), we successfully reconstructed a multi‐decadal, continuous spatiotemporal surface water temperature record for regulated rivers in the Columbia River Basin. Using 42 years of data, the surface water temperature could be predicted on average with 0.71° C of absolute error regardless of the dam's potential thermal influence in the downstream reaches. The reconstructed hydro‐thermal behavior generated from THORR revealed a long‐term downstream warming trend along the Columbia River. The open‐source THORR tool can be extended to any river system around the world that is not gauged with in‐situ temperature measurements for the reconstruction of hydro‐thermal behavior.

Impact of winch-assisted logging machinery on soil disturbance in the mountainous forests of Western Carpathians

IntroductionTimber harvesting on steep terrain is a challenge in terms of economic viability, safety, and environmental performance. Felling with chainsaws and use of yarders seems optimal in this environment. However, using mobile traction winches allows for the safe work of ground-based technologies even in these challenging conditions.MethodsOur study assessed the impact of winch-assisted cut-to-length harvesting on soil disturbance in young forest stands (up to 40 years old) across slopes of 14.9°-27.4° (27–52%). Utilizing 78 measurement points (i.e., 234 measurements), we analyzed soil samples from trail ruts, between ruts, and undisturbed areas for soil bulk density (g.cm−3) and soil moisture content (%), simultaneously measuring penetration resistance (MPa), penetration depth (cm) and rut depth (cm).ResultsThe results highlighted that areas without winch assistance experienced the most significant increases in soil bulk density (up to 22.35%) and penetration resistance (up to 26.8%), though these differences were not statistically significant. Linear mixed effects models did not confirm a significant effect (p > 0.05) of a traction winch on the soil bulk density (g cm−3) and penetration resistance (MPa) in the ruts of the forwarding trails. Mean forwarding trail profile depths ranged from 4.63 to 7.28 cm, with the maximum depths between 10.86 and 17.25 cm, showing deeper ruts in non-assisted areas. Moreover, the presence of the traction winch (p < 0.05) significantly affected the maximal depth of the forwarding trail rut.ConclusionThe findings suggest that winch-assisted harvesting may mitigate soil disturbance (rut depths) on steep slopes, offering a sustainable option for utilizing ground-based machinery with reduced environmental impact.

Identification of Potential Landslides in the Gaizi Valley Section of the Karakorum Highway Coupled with TS-InSAR and Landslide Susceptibility Analysis

Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional soil erosion, and landslide disasters occur frequently along this section, which severely affects the smooth flow of traffic through the China-Pakistan Economic Corridor (CPEC). In this study, 118 views of Sentinel-1 ascending- and descending-orbit data of this highway section are collected, and two time-series interferometric synthetic aperture radar (TS-InSAR) methods, distributed scatter InSAR (DS-InSAR) and small baseline subset InSAR (SBAS-InSAR), are used to jointly determine the surface deformation in this section and identify unstable slopes from 2021 to 2023. Combining these data with data on sites of historical landslide hazards in this section from 1970 to 2020, we constructed 13 disaster-inducing factors affecting the occurrence of landslides as evaluation indices of susceptibility, carried out an evaluation of regional landslide susceptibility, and identified high-susceptibility unstable slopes (i.e., potential landslides). The results show that DS-InSAR and SBAS-InSAR have good agreement in terms of deformation distribution and deformation magnitude and that compared with single-orbit data, double-track SAR data can better identify unstable slopes in steep mountainous areas, providing a spatial advantage. The landslide susceptibility results show that the area under the curve (AUC) value of the artificial neural network (ANN) model (0.987) is larger than that of the logistic regression (LR) model (0.883) and that the ANN model has a higher classification accuracy than the LR model. A total of 116 unstable slopes were identified in the study, 14 of which were determined to be potential landslides after the landslide susceptibility results were combined with optical images and field surveys. These 14 potential landslides were mapped in detail, and the effects of regional natural disturbances (e.g., snowmelt) and anthropogenic disturbances (e.g., mining projects) on the identification of potential landslides using only SAR data were assessed. The results of this research can be directly applied to landslide hazard mitigation and prevention in the Gaizi Valley section of the Karakorum Highway. In addition, our proposed method can also be used to map potential landslides in other areas with the same complex topography and harsh environment.

Seasonal snow–atmosphere modeling: let's do it

Abstract. Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes.

A Transdisciplinary Analysis of the 2009 Catastrophe in Giampilieri Superiore by Land Use Evolution Over Time

AbstractOn October 1 2009 a deluge hit the Peloritani Mountains, in NE Sicily, Italy, where the rainfall data recorded 225 mm in 9 h. Giampilieri Superiore was the most affected townlet in terms of infrastructural damages and human losses (37 dead, 37 wounded, and more than 1000 evacuees). The debris flows, triggered by flash floods wreaked havoc of the urban structure. More than 200 mln € was granted to protection works for conveying water and debris flows to bypass the town. The disaster was numerically simulated by SCIDDICA (Digital Twin) with excellent approximation, and the effects of the safety installation were tested as well, proving to offer sufficient protection for the village. The Peloritani area overlooking the Ionian See flourished during the Saracen domination, whose legacy of systems of terraces, cisterns and wells guaranteed the finest water management in steep terrains, allowing a safe emplacement of watermills, around which Giampilieri and other villages developed. This hydraulic legacy has been undermined during the last decades due to lack of appropriate maintenance because of the progressive loss of the Saracen culture. It is unconceivable that subsidies on the scale granted to Giampilieri are sustainable when facing innumerable future climatic disasters. A cheaper and more viable solution is to be found, e.g., the settlement of immigrants in the area, promoting the labor-social inclusion of those persons who already possess such a Saracen culture or are willing to acquire it from the last local depositaries, according to a retro-innovation perspective and methodology.

Application of fallout radionuclide—137Cs for estimating soil erosion in steep hillslopes with diverse land use of North-western Indian Himalayas

Hilly and mountainous regions are significantly impacted by soil erosion, primarily due to rainfall-runoff processes occurring on the hillslope scale. Assessing soil erosion is crucial for quantifying the loss of soil carbon and nutrients, which diminishes the potential of soil ecosystem services and is critical for mitigating the impacts of climate change on food security. However, the Himalayan landscape poses serious challenges for assessing soil erosion due to its steep and rugged terrain, which hinders the use of conventional and modelling methods. The fallout radionuclide—137Cs has been extensively utilized as an environmental marker for investigating soil redistribution processes. Despite its potential, there is a notable lack of 137Cs-based soil erosion studies in the Himalayan region. In this context, we assessed the applicability of the fallout radionuclide—137Cs method in quantifying soil erosion rates and identifying erosion hotspots on two hillslopes of the Higher Himalayas. On the hillslope scale, we observed that soil erosion rates vary based on slope gradient and land use/land cover. Forested areas exhibited the lowest soil erosion rates compared to cultivated areas, while flat hillslope positions experienced lower erosion rates than steeper positions. The average net erosion rate for the Harsil hillslope varied among different hillslope positions, ranging from – 2.9 to – 15.6 t ha−1 yr−1. Similarly, in the Gangnani hillslope, the net erosion rates varied across different positions, ranging from – 5.6 to – 39 t ha−1 yr−1. Our findings confirm that the middle and lower hillslope positions are the most critical source areas with higher soil erosion rates, while hillslope positions with forest cover demonstrate the lowest erosion rates, identified as helpful in controlling soil erosion. The study has demonstrated the applicability of FRN as a soil erosion measurement method in the complex, rugged, and steep terrain of the Himalayas, highlighting the need for targeted conservation efforts to mitigate soil erosion and preserve ecosystem integrity.

Land degradation vulnerability mapping using geospatial techniques: a case study of Nandakini River basin, NW Himalaya, India

ABSTRACT Land degradation is a significant global environmental issue, and its prevention has become a serious concern of the twenty-first century, particularly in the context of anthropogenic impact and climate change. It is crucial to map and assess the grade of land degradation to determine vulnerable areas. The Indian Himalayan region faces heightened risks with its steep terrain, active tectonics, heavy precipitation, and human interventions. This study focuses on the Nandakini Watershed. Employing AHP technique, the study calculates the Land Degradation Vulnerability Index (LDVI) using slope, terrain ruggedness, rainfall, temperature, LULC, soil depth, and organic carbon content. The results indicate that the upper region is highly vulnerable, and a higher slope influences the runoff in the area, followed by factors like NDVI and rainfall, collectively contributing to 62% of the overall index. In this context, the upstream regions of the watershed display a high LDVI, while most of the area falls within the low LDVI category. This indicates that approximately 2.10% of the study area is highly susceptible to soil erosion. RUSLE-based estimates have been used to further validate the LDVI result. The integrated LDVI offers a valuable tool for prioritising soil conservation and disaster mitigation.

Enablers and Consequences of Landslide Outbreaks in Bududa District, Eastern Uganda

Landslides are rapid movements of rock and soil on the Earth's surface, currently ranked among the most catastrophic environmental disasters worldwide. Bududa district, located on the steep slopes of Mount Elgon in eastern Uganda, has experienced a rising incidence of severe landslides. However, the environmental and human-driven factors contributing to these disasters remain poorly understood, hindering effective intervention strategies. This study investigated the causes, intervention strategies, and impacts of the persistent landslides in Bududa, aiming to generate insights for optimal solutions. A descriptive cross-sectional study was conducted, utilizing pre-validated semi-structured questionnaires and Key Informant Interviews to collect data from a random sample of 212 respondents, including household heads and district leaders. Additionally, 38 soil samples from landslide-affected sites in Bududa were analysed through experiments at Makerere University. This was complemented by observational surveys and digital photography. Qualitative data were analysed using thematic synthesis and photographic interpretation, while quantitative data were processed with descriptive and inferential statistics in STATA version 15.0. Graphs were generated using GraphPad Prism® version 9.0.0. In the findings, factorial analysis of variance (FANOVA, p<0.05), showed that the high soil- organic content (p<0.001), bulky density (p=0.002) and water saturation levels (p=0.011), in addition to other environmental factors such as the steep terrain and heavy rains are the cardinal mediators of landslide outbreaks in Bududa, while soil porosity (p=0.718) and permeability (p=0.267) were not implicated. These environmental drivers were found to be noticeably moderated by the widespread incidence of unregulated anthropogenic activities, primarily human settlement, infrastructural development and monetary enterprises like agriculture and cutting of trees. While some positive outcomes of landslides were acknowledged such as survivors improving their livelihoods after relocation, there was strong consensus on the adverse impacts, including casualties and deaths, and significant damage to infrastructure, agriculture, the economy, and social services. The majority of respondents confirmed the widespread presence of awareness campaigns, multisectoral collaborations, and mechanical preventive measures like physical mapping. Most participants agreed that tree planting could reduce landslides and that external support would enhance coping strategies in Bududa more effectively than local resources. However, they were uncertain about the effectiveness of existing local defensive measures, such as slope geometry modification. Additionally, monitoring for future outbreaks, household micro-land use interventions, and promoting slope stability were perceived as inadequate. Conclusively, reliable evidence was generated, indicating that the main factors enabling the recurrent landslides in Bududa are tri-faceted. That is, natural topography and soil characteristics, climate, and human influence. Consequently, strengthening the cooperative participation of stakeholders from sectors related to those three facets may improve the results of interventions.

Drought sensitivity analysis of meteorological and vegetation indices in Dak Nong, Vietnam

ABSTRACT Drought poses an increasing threat to agricultural sustainability in Dak Nong, Vietnam. This study investigates the sensitivity of meteorological and vegetation indices (VIs) to drought conditions in the region. We analyzed 23 years (2000–2022) of data from 11 meteorological stations and remote sensing imagery covering various crop types. Our methodology involved correlating multiple VIs (VHI, VCI, TCI, and TVDI) with meteorological drought indices (SPI and SPEI) at different time scales. Results reveal a strong correlation (r > 0.7, p < 0.001) between the vegetation health index (VHI) and the standardized precipitation evapotranspiration index (SPEI), particularly at a 4-month time scale (SPEI4). This combination proved most effective for drought monitoring across diverse vegetation types. Spatial analysis identified drought-sensitive zones covering 10.8% of the province, with steep terrain and limited river density. These areas, predominantly occupied by perennial agriculture, annual crops, and production forests, show heightened vulnerability to water scarcity. Our findings provide a scientific basis for targeted drought management strategies, including establishing an early warning system using SPEI4 and VHI, implementing water-efficient agricultural practices, and prioritizing farmer support in high-risk areas. This study enhances drought resilience and sustainable water resource management in Dak Nong and similar tropical regions.