Search Results (40)

Heavy rainfall events, characterized by extreme downpours that exceed 100 mm per day, pose an intensifying hazard to the densely settled valleys of the western Himalaya; however, their coupling with expanding urban land cover remains under-quantified. This study mapped the spatiotemporal exposure of built-up areas to heavy-day rainfall (HDR) across Jammu, Kashmir, and Ladakh and the adjoining areas by integrating daily Climate Hazards Group InfraRed Precipitation with Stations product (CHIRPS) precipitation (0.05°) with Global Human Settlement Layer (GHSL) built-up fractions within the Google Earth Engine (GEE). Given the limited sub-hourly observations, a daily threshold of ≥100 mm was adopted as a proxy for HDR, with sensitivity evaluated at alternative thresholds. The results showed that HDR is strongly clustered along the Kashmir Valley and the Pir Panjal flank, as demonstrated by the mean annual count of threshold-exceeding pixels increasing from 12 yr⁻¹ (2000–2010) to 18 yr⁻¹ (2011–2020), with two pixel-scale hotspots recurring southwest of Srinagar and near Baramulla regions. The cumulative high-intensity areas covered 31,555.26 km², whereas 37,897.04 km² of adjacent terrain registered no HDR events. Within this hazard belt, the exposed built-up area increased from 45 km² in 2000 to 72 km² in 2020, totaling 828 km². The years with the most expansive rainfall footprints, 344 km² (2010), 520 km² (2012), and 650 km² (2014), coincided with heavy Western Disturbances (WDs) and locally vigorous convection, producing the largest exposure increments. We also performed a forecast using a univariate long short-term memory (LSTM), outperforming Autoregressive Integrated Moving Average (ARIMA) and linear baselines on a 2017–2020 holdout (Root Mean Square Error, RMSE 0.82 km²; measure of errors, MAE 0.65 km²; R² 0.89), projecting the annual built-up area intersecting HDR to increase from ~320 km² (2021) to ~420 km² (2030); 95% prediction intervals widened from ±6 to ±11 km² and remained above the historical median (~70 km²). In the absence of a long-term increase in total annual precipitation, the projected rise most likely reflects continued urban encroachment into recurrent high-intensity zones. The resulting spatial masks and exposure trajectories provide operational evidence to guide zoning, drainage design, and early warning protocols in the region. Full article

To study the long-term hydroclimate variability in the Satluj Basin, streamflow data was reconstructed using tree-ring width datasets from multiple taxa available from the Kullu Valley, western (Indian) Himalaya. Five ring-width tree-ring chronologies of three conifer tree taxa (Abies pindrow, Cedrus deodara, and Pinus roxburghii) significantly correlate with the streamflow during the southwest monsoon season. Based on this correlation, a 228-year (1787–2014 CE) June–August streamflow was reconstructed using average tree-ring chronology. The reconstruction accounts for 34.5% of the total variance of the gauge records from 1964 to 2011 CE. The annual reconstruction showed above-average high-flow periods during the periods 1808–1811, 1823–1827, 1833–1837, 1860–1863, 1876–1881, and 1986–1992 CE and below-average low-flow periods during the periods 1792–1798, 1817–1820, 1828–1832, 1853–1856, 1867–1870, 1944–1947, and 1959–1962 CE. Furthermore, a period of prominent prolonged below-average discharge in the low-frequency streamflow record is indicated during the periods 1788–1807, 1999–2011, 1966–1977, 1939–1949, and 1854–1864. The low-flow (dry periods) observed in the present streamflow reconstruction are coherent with other hydroclimatic reconstructions carried out from the local (Himachal Pradesh and Kashmir Himalaya) to the regional (Hindukush mountain range in Pakistan) level. The reconstruction shows occurrences of short (2.0–2.8 and 4.8–8.3 years) to medium (12.5 years) periodicities, which signify their teleconnections with large-scale climate variations such as the El Niño–Southern Oscillation and the Pacific Decadal Oscillation. Full article

Rock glaciers are crucial freshwater resources, yet detailed knowledge about their distribution, characteristics, and dynamics in the Himalayan region is scarce. This study presents a comprehensive rock glacier inventory of the Jhelum basin, Kashmir Himalaya, India, using high-resolution Google Earth data. We identified 240 rock glaciers covering an area of 41.24 ± 2.2 km², with ~76% classified as active, ~20% inactive, and 3.7% relict. The average areas and lengths of these rock glacier types were 0.19 km², 0.06 km², and 0.29 km², and 699 m, 426 m, and 952 m, respectively. Most rock glaciers (~90%) were oriented northwards (N, NE, NW), while only 5% faced southwards (S, SE, SW). The lower limit of permafrost in the Jhelum basin is about 3316 m asl. Furthermore, we estimated the ice storage of rock glaciers in the Jhelum basin at 0.80 ± 0.13 km³, equivalent to 0.72 ± 0.12 km³ of water volume. This study enhances our understanding of permafrost distribution and the characteristics and dynamics in the basin. Given their greater resilience to climate change compared to clean glaciers, the hydrological significance of rock glaciers is expected to increase under projected climate change scenarios. This study highlights their importance as a vital water resource amidst the accelerated recession of clean glaciers. Full article

Forest fires have significant impacts on economies, cultures, and ecologies worldwide. Developing predictive models for forest fire probability is crucial for preventing and managing these fires. Such models contribute to reducing losses and the frequency of forest fires by informing prevention efforts effectively. The objective of this study was to assess and map the forest fire susceptibility (FFS) in the Indian Western Himalayas (IWH) region by employing a GIS-based fuzzy analytic hierarchy process (Fuzzy-AHP) technique, and to evaluate the FFS based on forest type and at district level in the states of Jammu and Kashmir, Himachal Pradesh, and Uttarakhand. Seventeen potential indicators were chosen for the vulnerability assessment of the IWH region to forest fires. These indicators encompassed physiographic factors, meteorological factors, and anthropogenic factors that significantly affect the susceptibility to fire in the region. The significant factors in FFS mapping included FCR, temperature, and distance to settlement. An FFS zone map of the IWH region was generated and classified into five categories of very low, low, medium, high, and very high FFS. The analysis of FFS based on the forest type revealed that tropical moist deciduous forests have a significant vulnerability to forest fire, with 86.85% of its total area having very high FFS. At the district level, FFS was found to be high in sixteen districts and very high in seventeen districts, constituting 25.7% and 22.6% of the area of the IWH region. Particularly, Lahul and Spiti had 63.9% of their total area designated as having very low FSS, making it the district least vulnerable to forest fires, while Udham Singh Nagar had a high vulnerability with approximately 86% of its area classified as having very high FFS. ROC-AUC analysis, which provided an appreciable accuracy of 79.9%, was used to assess the validity of the FFS map produced in the present study. Incorporating the FFS map into sustainable development planning will assist in devising a holistic strategy that harmonizes environmental conservation, community safety, and economic advancement. This approach can empower decision makers and relevant stakeholders to take more proactive and informed actions, promoting resilience and enhancing long-term well-being. Full article

The Himalayan region is a hotspot in terms of expected future hydrological and geomorphological variations induced by climate change on proglacial areas and the related implications for human societies established along the downstream rivers. Due to the remoteness of the proglacial zones in the Himalayas and the associated logistical problems in carrying out traditional field and UAV-based morphological monitoring activities, remote sensing here plays a crucial role to monitor past and current fluvial dynamics, which could be used to anticipate future changes; however, there has been, so far, limited research on morphological changes in Himalayan proglacial rivers. To address this gap, a morphological classification model was designed to classify recent changes in Himalayan proglacial rivers using the Google Earth Engine platform. The model is the first of its kind developed for the Himalayan region and uses multispectral S-2 satellite data to delineate submerged water channels, vegetated surfaces, and emerged, unvegetated sediment bars, and then to track their variations over time. The study focused on three training sites: Langtang-Khola (Nepal), Saltoro (Pakistan), and Nubra (Jammu and Kashmir) rivers, and one testing site, the Ganga-Bhagirathi River (India). A total of 900 polygons were used as training samples for the random forest classifier, which were further divided into 70% calibration and 30% validation datasets for the training sites, and a separate validation dataset was acquired from the testing site to assess the model performance. The model achieved high accuracy, with an average overall accuracy of 96% and a kappa index of 0.94, indicating the reliability of the S2 data for modeling proglacial geomorphic features in the Himalayan region. Therefore, this study provides a reliable tool to detect past and current morphological changes occurring in the Himalayan proglacial rivers, which will be of great value for both research and river management purposes. Full article

Insufficient research has been conducted on musk deer species across their distribution range, primarily because of their elusive behaviour and the fact they occupy remote high-altitude habitats in the Himalayas above 2500 m. The available distribution records, primarily derived from ecological studies with limited photographic and indirect evidence, fail to provide comprehensive information on the species distribution. Consequently, uncertainties arise when attempting to determine the presence of specific taxonomic units of musk deer in the Western Himalayas. This lack of knowledge hampers species-oriented conservation efforts, as there need to be more species-specific initiatives focused on monitoring, protecting, and combatting the illegal poaching of musk deer for their valuable musk pods. We used transect surveys (220 trails), camera traps (255 cameras), non-invasive DNA sampling (40 samples), and geospatial modelling (279 occurrence records) to resolve the taxonomic ambiguity, and identify the suitable habitat of musk deer (Moschus spp.) in Uttarkashi District of Uttarakhand and the Lahaul–Pangi landscape of Himachal Pradesh. All the captured images and DNA-based identification results confirmed the presence of only Kashmir musk deer (KDM) (Moschus cupreus) in Uttarakhand and Himachal Pradesh. The results suggest that KMD inhabit a narrow range of suitable habitats (6.9%) of the entire Western Himalayas. Since all evidence indicates that only KMD are present in the Western Himalayas, we suggest that the presence of other species of musk deer (Alpine musk deer and Himalayan musk deer) was wrongly reported. Therefore, future conservation plans and management strategies must focus only on KMD in the Western Himalayas. Full article

Karst springs are important water sources for both human needs and environmental flows. The responses of karst springs to hydrometeorological factors vary depending on local conditions. In this study, we investigated Martandnag spring in the Liddar catchment in the Kashmir valley of northern India. We used statistical time series (autocorrelation and cross-correlation) and machine-learning (ML) techniques (random forest regression (RFR) and support vector regression (SVR)) to characterize how rainfall, temperature, and snow cover affect the karst spring flow and predict the future responses of the spring stage based on climate scenarios, in the Intergovernmental Panel on Climate Change Assessment Report 6. The statistical time series showed that the memory effect of Martandnag spring varies from 43 to 61 days, indicating moderate karstification and a relatively high storage capacity of the karst aquifer in the Liddar catchment. The delay between recharge and discharge varies from 13 to 44 days, and it is more strongly correlated to snow/ice melt than to rainfall. The ML analysis shows that SVR outperformed RFR in predicting spring flow. Under all climate scenarios, a trained SVR model showed that spring flow increased during the late winter to early spring, and decreased during the summer (except in August) and in autumn. Scenarios with increased greenhouse gas emissions further reduced flow in the summer and autumn. These predictions can be helpful for water-resource planning in similar watersheds in the Western Himalayas. Full article

Studies from different parts of the world have generated pieces of evidence of climate change’s effects on plant phenology as indicators of global climate change. However, datasets or pieces of evidence are lacking for the majority of regions and species, including for the climate-sensitive Himalayan biodiversity hotspot. Realizing this gap in information, and the wide-ranging implications of such datasets, we integrated real-time field observations and long-term herbarium records to investigate the changes in the spring flowering phenology of Olea ferruginea Royle, commonly known as the Indian Olive, in response to the changing climate in the western Himalayas. We attempted to create phenological change model using the herbarium records and field observations after recording the current dates of flowering and overall temperature trends from the study area over the last four decades from the five regional meteorological observatories of the Jammu province managed by Indian Meteorological Department (IMD) in Jammu and Kashmir. When considering current flowering dates along with herbarium information (years 1878–2008) for O. ferruginea, our Generalized Additive Model (GAM) showed 15–21 days-early flowering over the last 100 years significantly (p < 0.01). Results of the Mann–Kendall test showed increasing trends of T_Min for all seasons significantly (p < 0.05) for Jammu province whereas T_Max was only for the spring season. The increasing T_Min of spring, summer, and autumn seasons also influenced the flowering phenology of O. ferruginea significantly (p < 0.01). By demonstrating the integrated use of methodological tools for finding long-term phenological changes in response to climate change, this work bridges knowledge gaps in phenological research from the developing world in general and the Himalayas in particular. Full article

The yield and nutritional profile of grass and legume species in Kashmir Valley’s rangelands are scantly reported. The study area in this paper included three types of sites (grazed, protected, and seed-sown) divided into three circles: northern, central, and southern Kashmir. From each circle, three districts and three villages per district were selected. Most sites showed higher aboveground biomass (AGB) compared to belowground biomass (BGB), which showed low to moderate effects on biomass. The comparison between northern, central, and southern Kashmir regions revealed that AGB (86.74, 78.62, and 75.22 t. ha⁻¹), BGB (52.04, 51.16, and 50.99 t. ha⁻¹), and total biomass yield (138.78, 129.78, and 126.21 t. ha⁻¹) were the highest in central Kashmir region, followed by southern and northern Kashmir regions, respectively. More precisely, AGB and total biomass yield recorded the highest values in the protected sites of the central Kashmir region, whereas BGB scored the highest value in the protected sites of southern Kashmir region. The maximum yield (12.5 t. ha⁻¹) recorded among prominent grasses was attributed to orchard grass, while the highest crude fiber and crude protein contents (34.2% and 10.4%, respectively), were observed for Agrostis grass. The maximum yield and crude fiber content (25.4 t. ha⁻¹ and 22.7%, respectively), among prominent legumes were recorded for red clover. The highest crude protein content (33.2%) was attributed to white clover. Those findings concluded the successful management of Kashmir rangelands in protected sites, resulting in high biomass yields along with the considerable nutritional value of grasses and legumes. Full article

Abies pindrow is a keystone tree species of temperate forests in the Himalayan range with immense ecological significance. The current study was designed to investigate the spatial distribution, population structure, associated flora, and sustainability of Abies pindrow in the temperate forests of Azad Jammu and Kashmir (AJK), Pakistan. Vegetation data were collected from 48 forest sites distributed in six districts of AJK with respect to the geography, microclimates, and vegetation structure by employing a systematic quadrate-based methodology. Abies pindrow populations were characterized by an average stem density of 183.9 trees/ha with an average basal area cover of 789 cm. A. pindrow populations showed a regeneration value of 555.6 seedlings/ha. A digital elevation model revealed that A. pindrow exhibited a large extent of distribution in an altitudinal range of 1800–3400 m. GIS analysis identified that north-facing slopes with a moderate degree of slope steepness constitutes the preferred habitat of the species in the Kashmir mountains. A floristic analysis revealed that a total of 282 species from 74 plant families comprised the associated flora of A. pindrow-dominated forests with Pinus wallichiana, Picea smithiana, Aesculus indica, and Viburnum grandiflorum as codominant companion species. A. pindrow forests exhibited significant levels of species diversity and richness with average values of Simpson’s diversity as 0.94, Shannon’s diversity as 3.09, species richness as 1.45, and maturity index value as 45.9%. The A. pindrow populations in the study area were found to be subjected to significant deforestation pressure along with overgrazing and erosion impacts. Results provide valuable scientific information for the conservation management of A. pindrow populations, ensuring the sustainability of temperate forest ecosystems in the Western Himalayan region of Kashmir. Full article

The Kashmir Valley is immensely susceptible to soil erosion due to its diverse topography and unstable geological formations in the Himalayan region. The present study helps in assessing the spatial distribution and prioritizing soil erosion zones in the Central Kashmir region covering the Sindh and Dachigam catchments. The study implemented the GIS-based analytic hierarchy process (AHP) and weighted sum method (WSM) using datasets of precipitation, geological map, soil map, and satellite imagery and derived eleven factors (topographical derivatives, LULC, soil, drainage, rainfall, lithology, wetness index and greenness of an area). The ratings and weightage were proven to be unbiased and reliable based on the observed value of the consistency ratio (C_R) (i.e., 0.07). The study depicts 41% of the total area to be extremely vulnerable to soil erosion. The slope varies from 0–62° with mean of 22.12°, indicating 467.99 km² (26%) and 281.12 km² (15%) of the area under high and very high susceptible zones, respectively. The ND_VI and ND_WI maps indicate soil erosion severity covering an area of 40% and 38%, respectively, in highly susceptible zones. High drainage density and curvature zones were observed in 18.33% and 22.64% of the study area, respectively. The study will assist in the planning and implementation of conservation measures. Full article

Phaseolus vulgaris L. is the major pulse cultivated and culturally inculcated in the food habits of the locals in the Himalayan mountainous region of Azad Jammu and Kashmir (AJK), Pakistan. The current study was designed to investigate the role of P. vulgaris cultivation in providing livelihood support and to evaluate its production and consumption patterns correlated with the household variables in the state of AJK. The socio-economic data was collected from nine bean cultivated areas in six districts of AJK. The data was acquired by administrating a total of 522 detailed semi structured questionnaires from a diverse array of the respondents following the snowball technique focusing on yield, consumption, revenue generation and livelihood support provided by bean cultivation. The results revealed that common bean cultivation provided significant livelihood support to the local mountainous populations with an average annual income of 50.80 $/family. Subsequently, bean production contributed an average annual per capita income of 6.81 $ in the area, which was attributed to the large family size. Local populations showed an average bean production of 33.93 kg/family, whereas the average annual bean consumption was recorded as 31.99 kg/family in the region. Bean crops were recorded to have an average price of $1.49/kg, with significant variations in the study area correlated with local yield. A data analysis indicated a strong correlation in bean production and consumption patterns. Common bean farmers had a very small farm size, averaging 0.24 ha, where 100% of farmers cultivated common beans as an intercrop with Maize as the primary crop. A Pearson’s test (p value < 0.05) revealed significant correlations between land holding and bean production as well as consumption, and bean production with annual per capita income. Small farm size, declining soil fertility, low bean pricing and the unavailability of market mechanisms were identified as the major challenges faced by the common bean farmers. It is recommended to employ an integrated bean farming approach to enhance the economic impact of common bean cultivation in the socioeconomic appraisal of the local populations. Full article

We assessed, for the first time, the plant assemblages in coniferous forests of temperate and alpine ecosystems of the Himalayas to understand the diversity of species and their phenological behaviours that lead to different growth forms in the climax forest community. In this study, we selected the coniferous forests of Bhallesa Hills, situated in Pir Panjal Mountain (Jammu and Kashmir) of the Himalayan biodiversity hotspot as a study area and used the quadrat method to document the floristic diversity over four years (2018–2021). The study sites were divided into four sub-sites (Chilli, Kahal, Chanwari, Gandoh), and at each site, 25 replicated plots (each measuring 2500 m², 50 × 50 m²) were established for repeated surveys and documentation. We then analysed species diversity, lifeforms, phenology and leaf size spectra of coniferous plant communities. We consulted various pieces of literature to understand native and non-native plants. The results showed that the species diversity and species richness, growth forms and phenology varied in the experimental plots. In total, we found 328 plant species belonging to 228 genera and 78 families from different localities of various growth forms. Approximately 68.51% of the plant species were native, and 31.49%of the species were non-native. In angiosperms, dicotyledon species were found to be dominant, with 83.23% of the total plant species, while the family Asteraceae was common, with 38 species. The biological spectrum analysis showed 29% of the species were chamaephytes, followed by 28% as therophytes and 21% as phanerophytes. We observed that plant communities respond differently to the existing environment drivers, with chamaephyte and therophytes being more tightly linked to temperate mixed-coniferous and alpine ecosystems, affected by climates and the availability of substrates for their growth and existence. The leaf size spectra analyses showed nanophyll (42.81%) as the dominant group. Conservation-prioritised species (IUCN, regional most threatened species in India), such as Taxus wallichiana Zucc., Picrorhiza kurroa Royle ex Benth., Trillium govanianum Wall. ex D.Don, Aconitum heterophyllum Wall. ex Royle and Euphorbia obovata Decne were found to be the most endangered plants. The results indicated more indigenous species, but there is a slow process of depletion of wild species, leading to colonisation by exotic alien species. This study indicated forests of the Himalayan regions are degrading at a faster rate, species are showing a shift in phenological behaviour due to anthropogenic factors leading to climate change, and indigenous species need conservation measures. Full article

The knowledge about the spatial distribution of soil organic carbon stock (SOCS) helps in sustainable land-use management and ecosystem functioning. No such study has been attempted in the complex topography and land use of Himalayas, which is associated with great spatial heterogeneity and uncertainties. Therefore, in this study digital soil mapping (DSM) was used to predict and evaluate the spatial distribution of SOCS using advanced geostatistical methods and a machine learning algorithm in the Himalayan region of Jammu and Kashmir, India. Eighty-three soil samples were collected across different land uses. Auxiliary variables (spectral indices and topographic parameters) derived from satellite data were used as predictors. Geostatistical methods—ordinary kriging (OK) and regression kriging (RK)—and a machine learning method—random forest (RF)—were used for assessing the spatial distribution and variability of SOCS with inter-comparison of models for their prediction performance. The best fit model validation criteria used were coefficient of determination (R2) and root mean square error (RMSE) with resulting maps validated by cross-validation. The SOCS concentration varied from 1.12 Mg/ha to 70.60 Mg/ha. The semivariogram analysis of OK and RK indicated moderate spatial dependence. RF (RMSE = 8.21) performed better than OK (RMSE = 15.60) and RK (RMSE = 17.73) while OK performed better than RK. Therefore, it may be concluded that RF provides better estimation and spatial variability of SOCS; however, further selection and choice of auxiliary variables and higher soil sampling density could improve the accuracy of RK prediction. Full article

Cichorium intybus L. (chicory) is an important medicinal plant with significant economic potential and has recently gained rapid momentum in the functional food sector. In the present study, soil chemistry, phytochemical, and molecular diversity were assessed for 50 accessions of chicory collected from diverse agro-climatic zones. In total, 64 common metabolites were identified from the leaves of 7 chicory accessions collected from different altitudes and among them, the predominant metabolites included methyl commate B (6.3–10.14%), gamma sitosterol (2.79–9.3%), and 9, 12, 15-octadecatrienoic-acid (2.55–8.42%). Three terpenoid compounds, viz., betulin, kolavelool and betulinaldehyde, were observed at high altitudes (1790, 1901, and 2172 m) and not observed at low altitudes. Among these compounds, betulin had the highest concentration with an average value of 23.53% followed by kolavelool with 7.37% and betulinaldehyde with 7.21%. For molecular diversity analysis, 12 ISSR primers were selected for PCR amplification and 86 bands were generated with an overall polymorphism percentage of 67.44%. The observed Nei’s genetic diversity (H) and Shannon’s information indices (I) were highest for the Pulwama (CIN-PU) group of accessions (H = 0.222 ± 0.018; I = 0.343 ± 0.027) and lowest for the Baramulla (CIN-BM) group of accessions (H = 0.115 ± 0.019; I = 0.173 ± 0.028). The Analysis of Molecular Variance (AMOVA) analysis revealed 56% variation existing within the groups and 44% among the groups of chicory accessions. This study shows that chicory populations vary considerably in terms of their molecular and phytochemical composition as a function of their geographic location. Furthermore, this study demonstrates that chicory phytochemical and molecular diversity are significantly influenced by altitude, soil chemistry, and growing conditions. Using metabolomics and altitudinal variation, cluster analysis showed that geographic origin was correlated with diversity patterns. Full article