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ABSTRACTThe production and distribution of food is an important aspect of agricultural geography. Tracing the origin of crops is vital to ensuring future food security, especially in the context of geopolitical and climate change. Drawing on a case study of Kashmir, this article demonstrates how evidence from archaeobotany and ancient writings can be used to trace the geographies of apple domestication, cultivation and distribution. In doing so, it draws attention to the significance of trade networks; in this case those historically supported by the Silk Road.

Landslides are constantly increasing in the Himalayan region due to strong tectonic activities, soil erosion, heavy rainfall, and anthropogenic activities. Despite the severe impact of landslides in the Himalayan regions on human lives and property, there is an urgent need for research to assess landslide vulnerability map and make better decisions. Therefore, this study aims to predict an accurate landslide susceptibility using hyper-tuned random forest (RF) and deep neural network (DNN) in the Himalayan State of Uttarakhand. Sobol’s global sensitivity and Explainable Artificial Intelligence (XAI) were also used to evaluate the impact of sixteen priority landslide conditioning parameters (LCPs). The study shows that, in both models, the areas with “very high” and “high” susceptibility to landslides are 5.72–6.29% and 17.33–17.37% of the total area, respectively. Furthermore, the Receiver Operating Characteristic (ROC) curve and Precision-Recall (PR) curve show that the Random Forest (RF) model outperforms the Deep Neural Network (DNN) model with an AUC value of 0.95 and 0.95, respectively. Sobol’s sensitivity analysis for RF shows that elevation, annual rainfall and slope are the most influential parameters in LSM. The study presents an innovative approach that combines hyper-tuned ML and DL with XAI techniques to create a robust model for LSM that can be used in other regions of the world to assess susceptibility landslide.

This study introduces a new methodology that utilizes time–frequency analysis and deep learning to evaluate the post-earthquake damage analysis of RC frame structures, aiming to enhance assessment efficiency and accuracy. The acceleration signals are subjected to four distinct time–frequency approaches for a six-story RC frame building. To accurately assess the damage condition of the post-earthquake structure, a combination of optimal parameters in a post-earthquake damage assessment model based on a one-dimensional convolutional neural network (1D-CNN) and the Bayesian optimisation (BO) algorithm are employed. The results show that the proposed method achieves a 92.5% accuracy in damage assessment through the wavelet scattering method, which is known for its quick calculation speed. A conditional generative adversarial network (CGAN)-based seismic data generation technique is built to address the issue of inadequate damage sample data sets. By producing high-quality samples that closely resemble actual samples, the combination of wavelet scattering and seismic data generation model increases the accuracy of damage assessment to up to 90.5%. This can be particularly useful in situations when there are limited sample sizes.

Chamoli 2021 disaster on 7th February 2021 was among the most devastating hazards in the history of Uttarakhand, India. This study investigates the potential of the Seismological Network Around Tehri Region (SNATR) to analyze the seismic precursors of the event, while simultaneously determining the early warning potential in the region. Seismic signals recorded at various stations of the Seismological Network Around Tehri Region (SNATR) revealed the dynamic properties of the event. It has been observed that the nucleation phase (04:51:18 UTC) of the event initiated after the P- and S-wave arrival at 04:31:41 UTC and 04:38:41 UTC, respectively, at the nearest seismic station to the source from a M6 earthquake which occurred in Davao del Sur, Philippines on 7th February 2021 at 04:31:41 UTC. The time–frequency analysis based on the Wavelet Synchrosqueezed Transform (WSST) reveals an increase in the signal energy from the earthquake and a surge post the initiation of the rock-ice avalanche. The analysis reveals the timing of four different phases of the Chamoli event with the highest dominant frequency (16–18) Hz due to the ice-rock fall, and the lowest frequency (2–5) Hz at the nucleation phase prior to the event and the range of seismic triggering frequency was (0.01–0.2) Hz. The velocity of the surface wave induced after the detachment phase was estimated to be about 2.71 km/s. We computed the time-difference based on different stations of the seismic network for estimating the early warning prospects of the seismic network and determined a warning period of approximately 10 and 14 min at distances of 13 and 19.7 km at the impacted sites of Rishiganga Power Corporation (RGPC) Ltd.’s 13.2 Mw Project and (Nation Thermal Power Corporation (NTPC), Tapovan Vishnugad 512 Mw Project), respectively, from the source at Raunthi peak.

ABSTRACTResearchers have long been trying to identify and estimate the impacts on the hydrological regime of rivers caused due to anthropogenic activities. In the present study, IHA was used to analyse the effect of dams on the flow regime of the upper and middle Narmada River. A total of 33 indicators were analysed to calculate Hydrological alteration due to dams in Narmada River. Average HA Index values of all 33 parameters are relatively higher for middle Narmada at Mandleshwar (0.86) than that of upper Narmada at Burmanghat (0.57). The average extreme water conditions have decreased both in frequency and in duration. Specifically, fewer floods and less low flow events have occurred in the post-dam period. The main purpose of this article is to identify and quantify the variations in the hydrologic regime so that the cumulative impacts of anthropogenic activities on water resources and the aquatic ecosystems may be reduced using proper water resource management strategies.

Trace gases in the atmosphere (NO2: nitrogen dioxide; SO2: sulfur dioxide) have a major impact on both local and global air quality, human health, climate and ecological conditions. Therefore, the present study investigated 16 years (2005– 2020) of Ozone Monitoring Instrument (OMI) based NO2and SO2in Dobson unit (DU) spatiotemporal distributions and variability, SO2/NO2ratio, trends, and potential source contribution function (PSCF) across ocean and land areas of Jiangsu Province, China. Results demonstrated higher NO2and SO2concentrations (DU) over land (NO2: 0.58 and SO2: 0.56) than in the ocean (NO2: 0.30 and SO2: 0.38) due to more concentrated anthropogenic activities on land surfaces. There were significant seasonal variations in NO2and SO2, with winter being the highest and summer being the lowest. The SO2/NO2ratio shows land and ocean pollution is caused by NO2and SO2emissions from ships and industrial processes. Furthermore, OMI-based trace gases and anthropogenic emissions showed a good correlation (NO2vs NOx= 0.626 and SO2vs SO2emission = 0.871) across land surfaces than the ocean (NO2vs NOx= 0.366). NO2and SO2levels over land surfaces decreased significantly (at a 95% confidence level) compared to the ocean on annual and seasonal scales, which is attributed to a decrease in NOxand SO2emissions. Furthermore, PSCF analysis shows that local sources have a greater impact on air quality than long-distance sources over land and ocean. It is concluded from this study that Chinese air pollution control policies achieved a satisfactory improvement in Jiangsu's air quality by reducing NO2and SO2. It is therefore recommended to continue or extend these policies in the future to improve China’s air quality, which will benefits its citizens.

The dramatic mass loss of Tropical Andean glaciers under the influence of climate change has caused alterations in regional hydrological regimes, including development and expansion of glacial lakes, especially moraine-dammed lakes, supraglacial lakes and ice-dammed lakes. There is a broad consensus on Moraine-Dammed Glacial Lakes (MDGLs) to be commonly understood as potentially most dangerous lakes that can trigger Glacial Lake Outburst Floods (GLOFs). The GLOF event in that process is expected to negatively impact the downstream communities, agricultural assets and infrastructure. In this study, we have prepared an updated and detailed inventory of MDGLs in the Cordillera Blanca region of the Peruvian Andes. The multi-temporal satellite data (TM, ETM, OLI and Sentinel-2A) was used to analyze the changes in lake area over a period of 40 years from 1980 to 2020. A total of 38 MDGLs (size > 0.05 km2) covering an area of 10.30 km2, and located in the altitudinal zone ranging from 4155 to 4960 masl were identified and mapped. From 1980 (6.59 km2) to 2020 (10.3 km2), an expansion of 3.7 km2(35%) at an annual rate of 0.09 km2/year was observed in the lake area. This study also contributes in terms of developing a database of past GLOF events from an extensive literature survey to understand the hazard and disaster profile of the region for the period 1702–2020. A total of 28 GLOF events have been reported in the region which brought devastation to the surrounding communities. We conclude that the region is highly prone to GLOFs as understood from the occurrence of GLOFs in the past as well as from the current scenario of MDGLs.

AbstractClimate change poses significant challenges to the Himalayas, a region characterised by its fragile ecosystems and vulnerable communities dependent on environmental resources. Accurate climate data are crucial for understanding regional climatic variations and assessing climate change impacts, particularly in areas with limited observational networks. This study represents a pioneering effort in evaluating climatic fluctuations in the Jhelum basin, located in the North Western Himalayas, by utilising a diverse range of gridded meteorological datasets (APHRODITE, CHIRPS, CRU, and IMDAA) alongside observed climate data from the Indian Meteorological Department. The primary goal is to identify the most effective gridded climate data product for regions with limited data and to explore the potential of combining gridded data sets with observed data to understand climatic variability. Findings indicate a consistent upward trend in temperature across all datasets, with varying rates of increase. CRU records a rise of 1 °C in Tmaxand 1.6 °C in Tmin, while APHRODITE shows a Tmeanincrease of approximately 1 °C. IMDAA reports increases in Tmaxand Tmin. Observed mean annual Tmaxand Tminshow net increases of 1 °C and 0.6 °C, respectively. Regarding precipitation, all datasets except IMDAA exhibit an increasing trend, contrary to observed data, which decreases from 1266 mm to 1068 mm over 40 years. CHIRPS, CRU, and APHRODITE display increasing trends, while IMDAA aligns closely with observed data but tends to overestimate precipitation by about 30%. Our research identifies IMDAA as the most suitable gridded climate data for the Jhelum basin in the North-western Himalayas. Despite some discrepancies in precipitation trends, IMDAA closely aligns with observed data, providing valuable insights for scholars and policymakers navigating climate data uncertainties in complex environments. Our findings contribute to informed decision-making and effective climate change mitigation strategies in the region.

AbstractThe ongoing trend of warming climate has made Glacial Lake Outburst Floods (GLOFs) a major cryospheric hazard worldwide, especially in the Himalayas. GLOFs in the Himalayan region are mostly caused by moraine-dammed proglacial lakes and ice-dammed lakes. These sporadic disasters have resulted in significant loss of life and property. This study offers a comprehensive analysis of the GLOF hazard potential of a potentially dangerous proglacial lake (PDGL) in the Ladakh region. This research explores the GLOF threat from the lake using multi-criteria analysis and advanced 2D hydrodynamic modeling approaches. The mass balance response of the mother glacier, its flow dynamics, and glacier-lake interactions were examined for the past 22 years. The findings show that over this period, the PDGL has had a notable expansion of 78.7%, accompanied by a significant recession of 13.2% in its feeding glacier. The glacier has witnessed an average thickness loss of ⁓7 m at the rate of 0.32 m a−1during this period. The average, lowest, and maximum depth of the glacier were found to be 30.95, 14.30, and 50.57 m, respectively and the average velocity of the glacier was estimated as 3.38 m a−1. Because of the lake’s rapid expansion and steep surrounding slopes, it was classified as a high-hazard lake. The risk to the downstream community was assessed through 2D hydrodynamic modeling using the HEC-RAS tool. The maximum discharge under the worst-case scenario for the piping and overtopping failures was estimated as 3890.99 m3s−1and 5111.39 m3s−1, respectively. The area potentially under the threat of inundation was calculated to be 4.74 and 5.38 km2for the moderate and worst-case scenarios respectively. The expected maximum flood velocities range from 18.26 to 23.78 meters, respectively for the moderate and worst-case scenarios. At several locations in the downstream area, routed hydrographs representing the GLOF propagation were generated. The findings show that the flood wave in the worst-case scenario would arrive at the first settlement in 50 min, with a peak velocity of 12.36 m s−1. The potentially inundated area includes critical infrastructure such as bridges, residential houses, and roads. To mitigate the potential risk associated with this lake, a more detailed and on-site study is highly recommended.

Glaciers in the high-altitude basins of the Himalayan region are retreating and thinning under the influence of climate warming, resulting in the development and formations of numerous lakes of different types. Some of these glacial lakes are potentially dangerous and may burst and cause huge destruction to the property, infrastructure and loss of lives in the low-lying areas. The present study uses multi-temporal satellite data to analyze the changes in glacier and glacial lakes of Upper Ganga Basin (UGB) over a period of 24 years from 1990 to 2014. Furthermore, we evaluated the probable GLOF impacts of the Unnamed Lake (potentially dangerous) at important sites using the hydrodynamic model (HEC-RAS). The results show that the total area of the glaciers in the study area shrunk by 82 ± 12.7 km2over a 24 years of observation period. Glacial lakes, on the other hand, have significantly increased both in terms of number and area from 1990 to 2014. The number of lakes increased by 54 lakes (38.84%), whereas area has increased from 2.92 km2in 1990 to 3.12 km2in 2014, thus indicating an overall growth of 6.84%. In the worst-case scenario, the breach hydrograph with 75 m breach width (Bw) and 20 min breach formation time (Tf) has been considered for flood routing across the river channel, which gives different peak flood hydrograph and other significant flood wave parameters like water depth, flow velocity and water surface elevation. The resultant flood will arrive at the nearest location (Ghastoli area) located 12 km from the lake site at 1 h and 40 min after the breach initiation with a maximum peak discharge of 1404.56 m3/s and maximum flood depth and velocity of 6.64 m and 2.48 m/s respectively. At the last selected location located at a distance of 55 km from the lake outlet, the flood wave potentially inundates the area with in 4 h 10 min after the breach initiation with a maximum peak discharge of 1078.35 m3/s and maximum flood depth, velocity of 11.93 m and 4.50 m/s respectively. After the analysis of flood hydrographs at various locations (cross-sections) in the downstream region, it was observed that the peak flood and other flood wave parameters were at its highest at the locations with narrow flood channel, while the intensity of the potential flood decreases as it advances toward the lower area with wide river channel. The elevation and slope of the river bed are also key factors that determine the intensity of the flood at a particular location. This study recommends restricting the built-up along the flood channel and to limit the growth of settlements toward ecological fragile areas to lessen the impacts of the various natural hazards.

Climate change is strongly affecting the Himalayas. Geologically young and fragile, the Himalayas are sensitive to even minor changes in the climate. Regional warming in Himalayas has been observed between 0.15 and 0.60 °C per decade, which is higher than the mean global warming rate of 0.74 °C per 100 years. Consequent to this warming trend, the glaciological changes in Himalayas are obvious, which has resulted in the formation, expansion and disappearance of various types of glacial lakes. The dangerous lakes among these pose threat to downstream community and infrastructure. In this study, an attempt has been made to critically analyze the progress in Glacier Lake Outburst Floods (GLOF) research and understand its dynamism from multiple approaches through a meta-analysis of peer-reviewed scholarly literature for a period of 2001–2020. The study has found that the glacial lakes in the study region are increasing in number and expanding rapidly owing to the climate change and anthropogenic causes as the Glacier Lake Outburst Floods (GLOFs) are becoming common. The expansion rates of pro-glacial lakes connected to glaciers and moraine-dammed lakes are faster than other type of lakes. Findings from the studies on area change analysis in the region reveal that a number of expanding glacial lakes can emerge as potential sites for future GLOFs, hence need immediate monitoring and observation. Geospatial tools and techniques coupled with field investigations have been found as a potent tool for mapping the evolution and propagation of GLOF hazards resulting from accelerated shrinking and thinning of glaciers and continued lake expansion. Further, the satellite-based remote sensing and modeling has been found as an excellent tool for GLOF management as well as reconstruction of previous GLOF events and prediction of future outburst potential. The idea of this study is linked to the increased incidence of GLOF events in the Himalayan region. This study will help in better understanding of glacial lake expansion and provide scope for future research in devising risk management action plans of potential GLOFs by selecting expanding glacial lakes as case studies.

Climate change, a global challenge of our time has severely impacted mountain glaciers. This study presents a detailed and an updated glacier inventory of 2020 for the Upper Jhelum Basin (UJB), Kashmir Himalayas using Sentinel-2 data and documents 308 glaciers. The glaciers range in size from 0.01 to 10.51 km2and cover an area of 102.1 km2. All glaciers are located in the elevation zone between 3500 and 5000 masl. About 86% of glaciers are small in size (< 0.5 km2) and constitute 38.7% of total ice cover. However, glaciers with size greater than 0.5 km2are only 14% but constitute a major proportion 61.3% of glacier area. Majority of small glaciers are a result of defragmentation of larger ones, hence more vulnerable to melting. Multi-date satellite images were used to assess glacier fluctuations of selected glaciers (≥0.3) for the period 1990–2020. The total glacier area has reduced from 85.25 to 68.17 km2(20%) at the rate of 0.56 km2a−1. Glaciers in the lower elevation zone (3800–4200 masl) have lost about 35% of their area at the rate of 0.22 km2a−1. The analysis of meteorological data using Thel–Sen method and Mann–Kendall test before examining its impact on glacier mass loss reveals that a persistent warming trend in mean annual temperatures is observed over the region. This is strictly under the influence of global climate change having a potential to cause enhanced shrinking and thinning of glaciers in the study region.

This study aims to assess the short-temporal dynamics of glaciers in Sind watershed of Jhelum basin in North-western Himalayas. The spatio-temporal fluctuations in morphological parameters of glaciers were estimated for a period of 28 years from 1990 to 2018. This has been achieved by analyzing multi-temporal imageries from Landsat-5, Sentinel-2 and IRS LISS IV earth observation data using geospatial tools and supplemented with field observations. The total glaciated area of Sind basin has decreased from 56.73 Km2(1990) to 43.94 Km2(2018) with the loss of 22.5% during the period of 28 years. The number of glaciers has increased from 92 (1990) to 116 (2018) generally due to defragmentation of glaciers. Glaciers (> 1 Km2) have shown a maximum loss of −34.8% during the study period. Although contributing less to the total glacier area, glaciers found in low elevation zones (3800–4200 masl) have lost maximum area of around − 55.5%. Mean elevation of glaciers has increased from 4330 masl (1990) to 4390 masl (2018). Mean Equilibrium Line Altitude (ELA) of glaciers (> 0.5 Km2) has shifted from 4336 to 4432 masl with an annual upward shift of 3.4 m a−1. The trend analysis of meteorological parameters (Temperature and Precipitation) using Mann–Kendall test reveals that the seasonal and annual temperature (both Tmaxand Tmin) has shown a significant positive trend. While as precipitation (both annual and seasonal) has shown a negative trend. The continued glacier recession in the region is largely due to decreasing precipitation in the winter months (November–March) and increasing mean annual temperatures. The persistence of warming trend will result in continuous shrinkage and thinning of glaciers with drastic impacts on the hydrological regime of the region.

This study presents a comprehensive analysis of Alpine pastures in the Kashmir Himalayas through a multidisciplinary approach, combining remote sensing and field-based assessments for biomass estimation and time series analysis of the (NDVI) Index for the growing season from May to October 2022. The Alpine and Subalpine region of Kashmir was delineated using ALOS PALSAR Digital Elevation Model, and Landsat 8 imagery was classified using a maximum likelihood algorithm, revealing a total grassland area of 160,974 hectares. After grassland delineation Biomass estimation was carried out based on data collected from 18 pastures, each of which was subjected to a stratified sampling approach to establish four 1 m² quadrats, with two designated for grazed areas and two for ungrazed areas, this yielded average biomass yields of 20.87 t/ha and an average dry weight biomass of 5.16 t/ha. Pastures like Daksum (28.36 t/ha), Tragbal (28.22 t/ha), Krush (27.83 t/ha), Lung Marg (27.03 t/ha), observed high biomass availability, while moderate levels were found in locations like Gangbal (22.75 t/ha), Hangel Marg (22.68 t/ha), Dagwan (21.76 t/ha), Gumri (20.82 t/ha), Bangus (20.66 t/ha), Pir Galli (18.52t/ha), Maalish (18.21 t/ha), In contrast, lower biomass values were recorded in Mohand Marg (11.47 t/ha), and Thajwas (9.81 t/ha). These findings were complemented by (NDVI) metrics, which varied across sites. For example, high NDVI values were observed for sites such as Pir Gilli, Bangus, and Kud Marg, indicating a healthier vegetative profile with less impact of grazing during the grazing season. In contrast, pastures like Mohand Marg, Thajwas, Razdan, and Tragbal recorded moderate NDVI values, suggesting a moderate level of grazing impact. Pasture sites with lower NDVI values and high standard deviation, such as Hangel Marg and Gumari, witnessed high seasonal variability, suggesting a high grazing impact, besides other natural factors responsible, like early snowfall. The study emphasizes the need for ongoing, multifaceted ecological assessments for the sustainable management and conservation of these critical Alpine ecosystems.