Search Results (19)

The distribution of forest aboveground biomass density (AGBD) is a key indicator of carbon stock and ecosystem health in the Eastern Himalayas, which represents a global biodiversity hotspot that sustains diverse forest types across an elevation gradient from lowland rainforests to alpine meadows and contributes to the livelihoods of more than 200 distinct indigenous communities. This study aimed to identify the key factors influencing forest AGBD across this region by analyzing the underlying biophysical and anthropogenic drivers through machine learning (random forest). We processed AGBD data from the Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR and applied filtering to retain 30,257 high-quality footprints across ten ecoregions. We then analyzed the relationship between AGBD and 17 climatic, topographic, soil, and anthropogenic variables using random forest regression models. The results revealed significant spatial variability in AGBD (149.6 ± 79.5 Mg ha⁻¹) across the region. State-wise, Sikkim recorded the highest mean AGBD (218 Mg ha⁻¹) and Manipur the lowest (102.8 Mg ha⁻¹). Within individual ecoregions, the Himalayan subtropical pine forests exhibited the highest mean AGBD (245.5 Mg ha⁻¹). Topographic factors, particularly elevation and latitude, were strong determinants of biomass distribution, with AGBD increasing up to elevations of 2000 m before declining. Protected areas (PAs) consistently showed higher AGBD than unprotected forests for all ecoregions, while proximity to urban and agricultural areas resulted in lower AGBD, pointing towards negative anthropogenic impacts. Our full model explained 41% of AGBD variance across the Eastern Himalayas, with better performance in individual ecoregions like the Northeast India-Myanmar pine forests (R² = 0.59). While limited by the absence of regionally explicit stand-level forest structure data (age, stand density, species composition), our results provide valuable evidence for conservation policy development, including expansion of PAs, compensating avoided deforestation and modifications in shifting cultivation. Future research should integrate field measurements with remote sensing and use high-resolution LiDAR with locally derived allometric models to enhance biomass estimation and GEDI data validation. Full article

This study assesses the risks of glacial lake outburst floods (GLOFs) from moraine sediment dams around Gurudongmar Lake in the Northern Sikkim Himalayas at an elevation of 17,800 feet. It focuses on three moraine sediment dams, analysing the implications of slope failure on the upstream side and the downstream stability under steady seepage conditions, as well as the risks posed by permafrost thawing. Using a comprehensive methodology that includes geotechnical evaluations, remote sensing, and digital elevation models (DEMs), the research employs finite element analysis via PLAXIS2D for the stability assessment. The main findings indicate a stratification of sediment types: the upper layers are loose silty sand, while the lower layers are dense silty sand, with significant variations in shear strength, permeability, and other geotechnical properties. Observations of solifluctions suggest that current permafrost conditions enhance the dams’ stability and reduce seepage. However, temperature trends show a warming climate, with the average days below 0 °C decreasing from 314 (2004–2013) to 305 (2014–2023), indicating potential permafrost thawing. This thawing could increase seepage and destabilise the dams, raising the risk of GLOFs. Numerical simulations reveal that scenarios involving water level rises of 5 and 10 m could lead to significant deformation and reduced safety factors on both the upstream lateral dams and downstream front dams. The study emphasises the urgent need for ongoing monitoring and risk assessment to address the potential hazards associated with GLOFs. Full article

The Sikkim region of the Eastern Himalayas is highly susceptible to Glacial Lake Outburst Floods (GLOFs), a risk that has increased significantly due to rapid glacial retreat driven by climate change in recent years. This study presents a comprehensive evaluation of GLOF susceptibility in Sikkim, employing Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) models. Key factors influencing GLOF vulnerability, including lake volume, seismic activity, precipitation, slope, and proximity to rivers, were quantified to develop AHP and FAHP based susceptibility maps. These maps were validated using Receiver Operating Characteristic (ROC) curves, with the AHP method achieving an Area Under the Curve (AUC) of 0.92 and the FAHP method scoring 0.88, indicating high predictive accuracy for both models. A comparison of the two approaches revealed distinct characteristics, with FAHP providing more granular insights into moderate-risk zones, while AHP offered stronger predictive capability for high-risk areas. Our results indicated that the expansion of glacial lakes, particularly over the past three decades, has heightened the potential for GLOFs, highlighting the urgent need for continuous monitoring and adaptive risk mitigation strategies in the region. This study, in addition to enhancing our understanding of GLOF risks in Sikkim, also provides a robust framework for assessing and managing these risks in other glacial regions worldwide. Full article

Soil erosion and subsequent sedimentation pose significant challenges in the Sikkim Himalayas. In this study, we conducted an assessment of the impact of rainfall-induced soil erosion and sediment loss in South Sikkim, which falls within the Teesta Basin, employing Revised Universal Soil Loss Equation (RUSLE) and Sediment Yield Index (SYI) models. Leveraging mean annual precipitation data, a detailed soil map, geomorphological landforms, Digital Elevation Models (DEMs), and LANDSAT 8 OLI data were used to prepare the factorial maps of South Sikkim. The results of the RUSLE and SYI models revealed annual soil loss >200 t ha⁻¹ yr⁻¹, whereas mean values were estimated to be 93.42 t ha⁻¹ yr⁻¹ and 70.3 t ha⁻¹ yr⁻¹, respectively. Interestingly, both models displayed similar degrees of soil loss in corresponding regions under the various severity classes. Notably, low-severity erosion <50 t ha⁻¹ yr⁻¹ was predominantly observed in the valley sides in low-elevation zones, while areas with severe erosion rates >200 t ha⁻¹ yr⁻¹were concentrated in the upper reaches, characterized by steep slopes. These findings underscore the strong correlation between erosion rates and topography, which makes the region highly vulnerable to erosion. The prioritization of such regions and potential conservation methods need to be adopted to protect such precious natural resources in mountainous regions. Full article

In the present research, the open-source WRF-SFIRE model has been used to carry out surface forest fire spread forecasting in the North Sikkim region of the Indian Himalayas. Global forecast system (GFS)-based hourly forecasted weather model data obtained through the National Centers for Environmental Prediction (NCEP) at 0.25 degree resolution were used to provide the initial conditions for running WRF-SFIRE. A landuse–landcover map at 1:10,000 scale was used to define fuel parameters for different vegetation types. The fuel parameters, i.e., fuel depth and fuel load, were collected from 23 sample plots (0.1 ha each) laid down in the study area. Samples of different categories of forest fuels were measured for their wet and dry weights to obtain the fuel load. The vegetation specific surface area-to-volume ratio was referenced from the literature. The atmospheric data were downscaled using nested domains in the WRF model to capture fire–atmosphere interactions at a finer resolution (40 m). VIIRS satellite sensor-based fire alert (375 m spatial resolution) was used as ignition initiation point for the fire spread forecasting, whereas the forecasted hourly weather data (time synchronized with the fire alert) were used for dynamic forest-fire spread forecasting. The forecasted burnt area (1.72 km²) was validated against the satellite-based burnt area (1.07 km²) obtained through Sentinel 2 satellite data. The shapes of the original and forecasted burnt areas matched well. Based on the various simulation studies conducted, an operational fire spread forecasting system, i.e., Sikkim Wildfire Forecasting and Monitoring System (SWFMS), has been developed to facilitate firefighting agencies to issue early warnings and carry out strategic firefighting. Full article

Migmatites and partial melts are exposed in both the lower and upper package of the Higher Himalayan Crystallines (HHC) thrust sheet within the Sikkim Himalayas. Zircon monazite and quartz oxygen isotopic ratios from Yumthang Valley, North Sikkim, and Rathong Chuu, West Sikkim, have been used to identify their sources and equilibrium conditions. Monazites show homogeneous growth, whereas zircons show growth rings. U-Th-Pb data on monazite only indicate the latest metamorphic event. However, zircons show metamorphic rim growth between 36 and 24 Ma over their detrital core with trailing growth from 22 Ma to 15 Ma. Pervasive fluids have been interpreted in coeval development during metamorphism, as shown by monazite and zircon c. 30 Ma. The Th/U ratio of zircon is higher and variable with weak residual zoning in the samples from higher elevations. Quartz–metamorphic zircon oxygen fractionation suggests Teq > 600 °C, while quartz–monazite fractionation shows the same or lower temperatures. Multiple sources of melts in the HHC (even along a single valley) have been observed by δ¹⁸O of 7‰ to 10‰ in zircon and 5‰ to 9‰ in monazite. Zircon and monazite generated in the same rock have similar δ18O values. Monazite grown ~20 Ma in the lower elevation sample had a low δ¹⁸O, suggesting interaction with an external fluid. Full article

The Late Holocene fossil pollen records from the Zemu glacier, located in Yabuk, North Sikkim, in the eastern Himalayas, effectively generated quantitative climate reconstructions based on the transfer function model. The transfer function model was developed by establishing a modern pollen–climate calibration set from the temperate alpine belt of North Sikkim. A redundancy analysis was carried out to detect the pattern of variation of climatic variables in the modern pollen datasets. The mean annual precipitation (MAP) and mean temperature of the warming month (MTWA) had the strongest influence on the composition of the modern pollen samples among the climatic variables considered in the analysis. Proxy data in the form of fossil pollen records were analyzed for reconstructing past climate based upon the relationships between modern pollen vegetation assemblages and climatic patterns. Transfer functions for MAP and MTWA were developed with the partial least squares (PLS) approach, and model performance was assessed using leave-one-out cross-validation. The validated model was used to reconstruct MAP and MTWA for the last 2992 cal years BP (1042 BC) in North Sikkim. The variability observed in the reconstructions was analyzed for past global climatic events. It was further compared with the available regional and hemispheric proxy-based climate reconstructions. The reconstructions captured comparable Medieval Warm Period (MWP) and Little Ice Age (LIA)-like events from the Zemu glacier region. The fossil pollen data and climate reconstructions were further compared with the mineral magnetism data of the subsurface sediment profile. Full article

Global climate change is causing unprecedented impacts on biodiversity. In India, there is little information available regarding how climate change affects biodiversity at the taxon/group level, and large-scale ecological analyses have been lacking. In this study, we demonstrated the applicability of eBird and GBIF (Global Biodiversity Information Facility), and produced national-scale forecasts to examine the possible impacts of climate change on terrestrial avifauna in India. Using data collected by citizen scientists, we developed fine-tuned Species Distribution Models (SDMs) and predicted 1091 terrestrial bird species that would be distributed in India by 2070 on two climatic surfaces (RCP 4.5 and 8.5), using Maximum Entropy-based species distribution algorithms. Of the 1091 species modelled, our findings indicate that 66–73% of bird species in India will shift to higher elevations or shift northward, and 58–59% of bird species (RCP 4.5 and 8.5) would lose a portion of their distribution ranges. Furthermore, distribution ranges of 41–40% of bird species would increase. Under both RCP scenarios (RCP 4.5 and 8.5), bird species diversity will significantly increase in regions above 2500 m in elevation. Both RCP scenarios predict extensive changes in the species richness of the western Himalayas, Sikkim, northeast India, and the western Ghats regions by 2070. This study has resulted in novel, high-resolution maps of terrestrial bird species richness across India, and we predict predominantly northward shifts in species ranges, similar to predictions made for avifauna in other regions, such as Europe and the USA. Full article

The Himalayan state of Sikkim is prone to some of the world’s largest landslides, which have caused catastrophic damage to lives, properties, and infrastructures in the region. The settlements along the steep valley sides are particularly subject to frequent rainfall-triggered landslide events during the monsoon season. The region has also experienced smaller rock slope failures (RSF) after the 2011 Sikkim earthquake. The surface displacement field is a critical observable for determining landslide depth and constraining failure mechanisms to develop effective mitigation techniques that minimise landslide damage. In the present study, the persistent scatterers InSAR (PSI) method is employed to process the series of Sentinel 1-A/B synthetic aperture radar (SAR) images acquired between 2015 and 2021 along ascending and descending orbits for the selected areas in Gangtok, Sikkim, to detect potentially active, landslide-prone areas. InSAR-derived ground surface displacements and their spatio-temporal evolutions are combined with field investigations to better understand the state of activity and landslide risk assessment. Field investigations confirm the ongoing ground surface displacements revealed by the InSAR results. Some urban areas have been completely abandoned due to the structural damage to residential housing, schools, and office buildings caused by displacement. This paper relates the geotechnical investigations carried out on the ground to the data obtained through interferometric synthetic aperture radar (InSAR), focusing on the triggering mechanisms. A strong correlation between seasonal rainfall and landslide acceleration, as well as predisposing geological-structural setting, suggest a causative mechanism of the landslides. Full article

The Sikkim region of the Himalayas (NE India) may form an important microplate between Nepal and Bhutan. Here we report high-resolution pressure-temperature (P-T) paths taken from garnet-bearing rocks across the northern and eastern portion of the region’s Main Central Thrust (MCT) shear zone. The MCT separates units affiliated with the Greater Himalayan Crystallines (GHC) in its hanging wall from the Lesser Himalayan Formation (LHF). Late Miocene monazite ages are reported from the LHF (10–14 Ma), whereas those from the GHC are Miocene (18–20 Ma). Some paths from the LHF and GHC show a P decrease before burial, consistent with erosion before compression. MCT shear zone and GHC rocks show a P increase and then decrease over a short T interval. This hairpin P-T path is consistent with an imbrication model for the Himalayas. LHF P-T path conditions and those obtained using conventional thermobarometry are best in agreement. These paths also are consistent with observed mineral assemblages and garnet zoning. Although we have the most confidence in LHF results, MCT shear zone and GHC P-T path shapes suggest processes to establish imbrication tectonics may have occurred here as early as the Miocene. Full article

Studies on the assessment of land degradation, overland flow, soil loss, and nutrient loss have emerged as paramount importance for food security and rural livelihood in the mountains. The present study dealt with similar issues in the Eastern Himalayas, for which the primary data were collected from the field during 2017–18. Quantitative and qualitative methods were used to collect data on soil erosion and information on overland flow, soil loss, and nutrient loss was assessed through field experiments in the watershed of Sikkim, Eastern Himalayas. The first section of the methodology deals with the experimental analysis from different land use categories to quantify soil loss. In the second section, detailed qualitative analyses of farmers’ perceptions of soil erosion indicators were recorded through field surveys, i.e., key informant interviews (KEIs) and focus group discussions (FDGs). The results showed that the highest overland flow was in barren land (8.63%) followed by large cardamom-based agroforestry system (7.02%), and mixed cropping (4.84%), and the lowest overland flow was in terrace cultivation (4.69%). Soil loss was estimated to be the highest for barren land (7.73 Mg/ha/year (megagram/hectare/year)) followed by mixed cropping (4.32 Mg/ha/year), and terrace cultivation (3.75 Mg/ha/year), with the least soil loss estimated to be in cardamom-based agroforestry (3.23 Mg/ha/year). Loss of nitrogen (N) (4.49 kg/ha/year) and phosphorous (P) (2.43 kg/ha/year) were highest in barren land, while potassium (K) loss was highest (4.30 kg/ha/year) in mixed farming. The lowest N loss rate (3.34 kg/ha/year) was in terrace cultivation, the lowest P loss rate (8.19 kg/ha/year) was in mixed farming, and the lowest potassium loss rate (3.28 kg/ha/year) was in cardamom-based agroforestry. Approximately 33% of the farmers acknowledged light or no soil losses, while 17% of the farmers accepted moderate soil erosion. The results of field survey indicated that only 15–19% of the farmers reported high or extreme soil loss in the fields. Farmers in the watershed are practicing multiple measures to control land degradation; however, marginal farmers are still vulnerable and need strong support from the government to safeguard their land. Full article

The Sikkim Himalayan glaciers and glacial lakes are affected by climate change like other parts of the Himalayas. As a result of this climate variability in the Sikkim Himalaya, a detailed study of the Gurudongmar lake complex (GLC) evolution and outburst susceptibility assessment is required. Glacial lake volume estimation and lake outburst susceptibility assessment were carried out to reveal different characteristics for all four lakes (GL-1, GL-2, GL-3, and GL-4) from the lake complex. Each of these lakes has a moderate to very high potential to outburst. As the dam of GL-1 provides no retention capacity, there is a very high potential of a combined effect with the sudden failure of the moraine-dams of GL-2 or GL-3 located upstream. Temporal analysis of GLC using optical remote sensing data and in-field investigations revealed a rapidly increasing total lake area by ~74 ± 3%, with an expansion rate of +0.03 ± 0.002 km² a⁻¹ between 1962 and 2018 due to climate change and ongoing glacier retreat. The overall lake area expansion rates are dependent on climate-driven factors, and constantly increasing average air temperature is responsible for the enlargement of the lake areas. Simultaneously, changes in GLC expansion velocity are driven by changes in the total amount of precipitation. The deficit in precipitation probably triggered the initial higher rate from 1962 to 1988 during the winter and spring seasons. The post-1990s positive anomaly in precipitation might have reduced the rate of the glacial lake area expansion considerably. Full article

Agriculture is the major source of livelihood in rural areas and is considered the backbone of the Indian economy. In Sikkim, agriculture is being practiced by 80% of the rural population, and having no other major livelihood options has created immense pressure on the farmers and agricultural land. Agriculture sector is under great stress as the farmers are being confronted by various challenges in Sikkim Himalaya in recent years, such as land degradation, climate change and socio-economic problems. Despite the number of indigenous agriculture management methods being practised in Sikkim Himalaya, the agricultural production system is weakening. In this context, this paper presents an analysis of challenges faced by indigenous communities, local farmers and potential sustainable strategies for their management in Rani Khola watershed of Sikkim Himalaya. Data and information were collected by field observation, questionnaire surveys of 300 households, key informant interviews and focus group discussions conducted during 2017–18. Data processing and analysis were carried out with a combination of techniques, such as the application of remote sensing (RS), geographic information system (GIS)-based data processing and descriptive statistics. Major challenges identified in the watershed are water scarcity (80%), climate change (88%), soil erosion and runoff (72%), higher investment cost (100%), lack of irrigation facilities (77%), fragmentation and size of landholdings (100), human–wildlife conflict (59%) and pests and disease (60%). Some possibilities and innovations that could address these problems are the use and retaining of various indigenous soil and water conservation (SWC) measures, diversified farming systems, community involvement in the government development process, better irrigation facilities, strengthening the local economy, coordinated planning between stakeholders and development of market feedback mechanism within the system. Full article

The efficiency of deep learning and tree-based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree-based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslides conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning. Full article

The Sikkim–Bhutan seismic gap has witnessed a long earthquake quiescence since the 1714 M7.5~8.5 earthquake. The state of stress accumulation beneath the Sikkim–Bhutan Himalaya and its spatial correlation with seismicity remains unclear due to the lack of geodetic measurements and the low levels of seismic activity. We compile Global Positioning System (GPS) measurements in southern Tibet with the available velocities in the Sikkim–Bhutan Himalaya to reveal the characteristics of strain buildup on the Main Himalayan Thrust (MHT). We correct non-tectonic hydrological loading effects in a GPS time series to accurately determine the Three-Dimensional (3D) velocities of each continuous station. Extensive GPS measurements yield convergence rates of 16.2~18.5 mm/y across the Sikkim–Bhutan Himalaya, which is quite consistent with that observed elsewhere in the Himalaya. Based on a double-ramp structure of the MHT, a refined 3D coupling image is inverted using a dense network of GPS velocities. The result indicates significant along-strike variations of fault coupling beneath the Sikkim–Bhutan Himalaya. The locking width (coupling > 0.5) of western Bhutan reaches ~100 km, which is 30~40% wider than Sikkim and eastern Bhutan. An obvious embayment of decoupling zone near the border between Sikkim and western Bhutan is recognized, and coincides spatially with the rupture terminates of the 1934 Mw8.2 and the 1714 M7.5~8.5 earthquakes, indicating that the large megathrust earthquakes along the Sikkim–Bhutan Himalaya are largely segmented by the spatial variation of frictional properties on the MHT. Using a new compilation of seismic records in the Sikkim–Bhutan Himalaya, we analyze the spatial correlation between fault coupling and seismic activity. The result suggests that the seismicity in the Bhutan Himalaya is broadly distributed, instead of restricted in the lower edge of the interseismic locking zone. This implies that the seismic activity in the Bhutan Himalaya is not uniquely controlled by the stress accumulation at the downdip end of the locked portion of the MHT. Full article