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The Soil andWater Assessment Tool (SWAT) model combined with a temperature index and elevation band algorithm was applied to the Hunza watershed, where snow and glacier-melt are the major contributor to river flow. This study’s uniqueness is its use of a snow melt algorithm (temperature index with elevation bands) combined with the SWAT, applied to evaluate the performance of the SWAT model in the highly snow and glacier covered watershed of the Upper Indus Basin in response to climate change on future streamflow volume at the outlet of the Hunza watershed, and its contribution to the Indus River System in both space and time, despite its limitation; it is not designed to cover the watershed of heterogeneous mountains. The model was calibrated for the years 1998–2004 and validated for the years 2008–2010. The model performance is evaluated using the four recommended statistical coefficients with uncertainty analysis (p-factor and r-factor). Simulations generated good calibration and validation results for the daily flow gauge. The model efficiency was evaluated, and a strong relationship was observed between the simulated and observed flows. The model results give a coefficient of determination (R2) of 0.82 and a Nash–Sutcliffe Efficiency index (NS) of 0.80 for the daily flow with values of p-factor (79%) and r-factor (76%). The SWAT model was also used to evaluate climate change impact on hydrological regimes, the target watershed with three GCMs (General Circulation Model) of the IPCC fifth report for 2030–2059 and 2070–2099, using 1980–2010 as the control period. Overall, temperature (1.39 C to 6.58 C) and precipitation (31%) indicated increased variability at the end of the century with increasing river flow (5%–10%); in particular, the analysis showed smaller absolute changes in the hydrology of the study area towards the end of the century. The results revealed that the calibrated model was more sensitive towards temperature and precipitation, snow-melt parameters and Curve Number (CN2). The SWAT results also provided reliable information for the daily runoff from the sub-basin watersheds responding to changing climatic conditions. SWAT can thus be used to devise effective strategies for future sustainable water management in the region, while combating vulnerabilities against floods and water storage in downstream water reservoirs such as the Diamer-Basha dam.

Gilgit-Baltistan is known for the extraction of placer gold as well as base metals however, no significant work has been done to investigate the host rock. This study is mainly focused on identification of the host rock for metallic minerals of Gindai Yasin, Ghizer district of Gilgit Baltistan, located towards south-west of the Main Karakorum Thrust (MKT), a mega thrust separating Kohistan Island Arc from the Karakoram plate. The Shamaran volcanics consist of heterogeneous basaltic-andesite to andesite and calc-alkaline rock units. Petrographic results indicate that, the rocks of mineralised zone are felsic to mafic in composition, optical analysis showed that these rocks had gone through extrusive hydrothermal alteration. The rocks mostly comprised of metallic minerals include chalcopyrite, malachite, pyrite and azurite. From the geochemical analysis, a significant portion of the sample is basaltic-andesite to andesite in composition, and mineralised zones are mafic to ultramafic. Gold and copper concentrations varies from 2.43-4.76 ppm, and 26800-98600ppm respectively. [ABSTRACT FROM AUTHOR]

The hydrologiest are debating on the rise in global glacier melting. However, the situation in Karakoram glaciers seems not the same. Therefore, it is essential to measure the status of snow in the Karakoram region of (GB; Gilgit-Baltistan) Pakistan. To this end, the current study examines the dynamics of snow cover using Moderate Resolution Imaging Spectroradiometer (MODIS) 8-days sow-cover in response to in-situ meteorological indicators from four high altitude automatic weather stations in Upper Indus Basin (UIB). The results conclude that the 8-days snow cover images not only show the qualities of MODIS (high resolution) images it's also characterized the high accuracies for snow-cover classification (~85 to 95 %). We analysed the snow-cover dynamics in the UIB and computed the maximum quantity of snow cover days (SCD) in a year that shadowed an increasing trend from 2000-2014, with an increase in snow-covered area (SCA) of 45.04% to 59.26% of the total land area of GB. Besides, there is a slightly decreasing trend observed in the SCA of Hindukush, and Himalayan mountains region in the last 15 years, due to increases in temperature and precipitation. Spearman's correlation analysis is also obtained between monthly and annually snow-cover changes (snow accumulation /depletion over six elevation zones above 2000 m elevation) and temperature. The month of January shows decreasing trends for five elevation zones out of six zones below 6000 m elevation. In spring, particularly in March shows a decreasing trend for two zones above 5000m elevations, while shows increasing trend in autumn for three zones above 4000 m elevation. A negative trend observed between monthly SCA and Khunjerab area (mean temperature and precipitation), and similar results are also noted for the remaining three regions such as Deosai, Naltar and Shandoor of UIB. The study suggests that, in the UIB region, climate change also employ a significant influence on regional SCA and days, as well as the distribution of snow-cover. These findings help for the forecasting of seasonal flow in the area and signifying snow-cover as a possible predictor of climate change. [ABSTRACT FROM AUTHOR]

This study is an attempt to quantify the indoor and outdoor gamma radiation level, the associated effective dose rate and Lifetime Cancer Risk in Gahkuch, the capital city of district Ghizer in the eastern Hindukush. All measurements have been carried out by FAG radiation dose rate meter in representative houses made of different construction materials. These measurements were performed at standard height of 1 meter above the ground level. The mean absorbed dose rate was 229.5 nGy/h for indoor and 220.6 nGy/h for outdoor environments, which is greater than global level. Annual Effective Dose for indoor were determined as 1.0 mSv/y while for outdoor its value was 0.4 mSv/y, which differs to the average worldwide value of 0.48 mSv/y. Average Excess Lifetime Cancer Risk was found to be 3.4×10-3 for indoor while it was 1.4×10-3 for outdoor. Both of these values are higher than the World average value 0.29×10-3. The current study shows that Gahkuch, Ghizer is placed in those regions where a remarkably high dose rate exists. [ABSTRACT FROM AUTHOR]

To tackle the challenges of the growing water demands, an improved Water resource management is required. The main contributor of water resources in Pakistan is the Indus river, which Inflow largely depends on glacier and snowmelt from Upper Indus Basin located in the mountainous ranges of Himalaya-Karakoram-Hindukush (HKH). Despite such importance, assessment of cryosphere dynamics cannot be done manually with accuracy, especially in mountainous areas comprising difficult terrain. To obtain precision, other sources of information such as station data and remotely sensed snow-covered area, should be used together to make accurate conclusions about the snow cover and climate variability conditions which will affect downstream population for water-related activities and its management and, for example, hydropower generation, etc. Therefore, this study was done to explore the changes in snow-covered area in Shayok River basin (Sub-catchment of the Indus River) through remote sensing and GIS. Satellite images of MODIS sensor were used to examine the variation in snow. Modis 8-day composite images from 2000-2018 were selected to investigate SCA due to its high accuracy. Furthermore, climate data from one (only available) high altitude climate station located at the same basin was used to study correlation among now cover variations with varying preticipation, minimum and maximum year-round temperatures for the period of 2000-2018. Analysis showed that remote sensing is a useful technique for estimation of snow in difficult and complex topography. Moreover, results also indicate that SCA is highly correlated with the climatic parameters (Temperature and Precipitation data) therefore it is important to get accurate climate data to understand snow extent properly. Our results will be helpful to manage water resources properly in downstream population which indicate one-month shift of snow cover accumulation and depletion. However, to make more accurate and conclusions, we recommend on detail study of each river basin and long-term monitoring of whole UIB. [ABSTRACT FROM AUTHOR]

The Karakoram, Hindu Kush and Himalayan mountain areas of Pakistan (Gilgit-Baltistan) are known to have significant populations of globally important wildlife species, ungulates being prominent of these, which have never been studied systematically before. This study was conducted to investigate current population, distribution, conservation and habitat condition of the six major ungulate species i.e., Himalayan ibex (Capra ibex sibirica), Blue sheep (Pseudois nayaur), Astore markhor (Capra falconeri falconeri), Ladakh urial (Ovis vignei), Marco polo sheep (Ovis ammon polii) and Himalayan musk deer (Moschus chrysogaster) in Gilgit-Baltistan. Bi-annual census surveys using direct and indirect counting methods were held in 86 potential habitats (sub catchments) during 2005 to 2010 and questionnaire based interviews were held with local hunters and herders. Results revealed that C. ibex is the most common species, followed by P. nayaur and C. f. falconeri whereas Marco Polo sheep was limited to KNP, Blue sheep to Shimshal and Soqtarabad; Ladakh urial to lower reaches of Karakoram and western Himalayas. Musk deer is confined to rhododendron dominated birch forests of western Himalayas. The study also showed that population of trophy animals i.e., Blue sheep, H. ibex and A. markhor has increased considerably whereas that of non-trophy animals i.e., Ladakh urial and musk deer have fallen down to the verge of local extinction. The increases are attributed to overwhelming success of community base conservation program, initiated by WWF, AKRSP and GB Forest & Wildlife department in 1993 while failure to save non-trophy animals is possibly due to their low economic return and lack of community interest. Efforts to maintain balance between conservation needs of the wild resource and development needs of the dependent communities is required for sustainable management of the fragile mountain ecosystem in the region. © 2014 Friends Science Publishers [ABSTRACT FROM AUTHOR]

Avian diversity was studied for the period of two years from January 2011 to December 2012, within and around the Central Karakoram National Park including Nagar (Hisper, Hoper, Nagar-Khas, Askurdas, Summair and Shahyar valleys), Skardu (Shigar valley) and Ghanche (Mashabrum valley). A total of 108 species belonging to 75 genera, 38 families and 16 orders were identified in the study areas. Order Passeriformes was dominating with 57% species followed by Anseriformes (7%), Charadriiformes and Accipitriformes (6%) each, Columbiformes (5%), Galliformes and Falconiformes (4%) each, Pelecaniformes (3%), Coraciiformes and Gruiformes (2%) each, and Bucerotiformes, Apodiformes, Caprimulgiformes, Piciformes, Suliformes and Strigiformes (1%) each. Residential status indicated that 57 species were residents (including birds with winter and summer influx), 26 species were summer visitors and 25 were winter visitors. Relative abundance revealed that 38 species were uncommon, 37 were common, 17 were very common and 16 species were rare. The main threats to the avian diversity in the study area were degradation of habitat due to anthropogenic activities like shooting, trapping, poaching and use of pesticides on fruit orchards and vegetable fields. © 2014 Friends Science Publishers [ABSTRACT FROM AUTHOR]

The accurate detection and monitoring of supraglacial lakes in high mountainous regions are crucial for understanding their dynamic nature and implications for environmental management and sustainable development goals. In this study, we propose a novel approach that integrates multisource data and machine learning techniques for supra-glacial lake detection in the Passu Batura glacier of the Hunza Basin, Pakistan. We extract pertinent features or parameters by leveraging multisource datasets such as radar backscatter intensity VH and VV parameters from Sentinel-1 Ground Range Detected (GRD) data, near-infrared (NIR), NDWI_green, NDWI_blue parameters from Sentinel-2 Multi-spectral Instrument (MSI) data, and surface slope, aspect, and elevation parameters from topographic data. The entire dataset is partitioned into training and testing sets, with machine learning models including the artificial neural network (ANN), the support vector machine (SVM), logistic regression (LR), random forest (RF), and K-nearest neighbour (KNN) trained on the training data (70%). Accuracy assessment employs testing data and involves the evaluation of metrics such as ROC curves and confusion matrices. The best-performing model, ANN, is validated against manually digitized lake polygons derived from Sentinel-2 and Google Earth Pro imagery. Furthermore, the digitized lake polygons are used to analyze glacial lake dynamics from 2016 to 2022. Key findings of this research presented that the NDWI_green, Sigma0_VH, and elevation are the most significant predictors in detecting supra-glacial lakes. Among the various trained and evaluated models, the Artificial Neural Network (ANN) achieved the highest performance (accuracy: 95%, AUC: 0.99) and accurately mapped supra-glacial lakes regardless of their small size. The findings have significant implications for understanding glacial lake behavior in the context of climate change and informing future research and monitoring efforts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)