Hunza River Basin Research Articles

Assessing the supraglacier extent of Karakoram glaciers located in Hunza River Basin, Pakistan

ABSTRACT Measuring the extent of supraglacial debris cover (SDC) in the Karakoram region has proven to be a difficult task. Semi-automatic methodologies are often used for mapping the SDC area. However, these have limitations which lead to the overestimation or underestimation of glaciated areas. Considering these facts, this study aimed to assess the glacier's extent using a combination of satellite data and ground verification of SDC in the Hunza River Basin, Pakistan. The normalized difference snow index (NDSI) of various satellite images coupled with extensive ground survey was applied to estimate the glacier extent. Results of the glacier extents were in the range of 18–32 km2 for Gulkin, 6–18 km2 for Gulmit, and 6–17 km2 for Pissan glaciers. The ground survey indicated that satellite products are underestimating the extent of glaciers by an average of 18.018%. The comparison of on-site SDC data with Global Land Ice Measurements from Space (GLIMS) and Randolph Glacier Inventory (RGI) databases also indicates a slight variation. Overall results validate that combining satellite imagery with ground verification significantly enhances the accuracy of supraglacial extent assessment.

Ascertainment of Hydropower Potential Sites Using Location Search Algorithm in Hunza River Basin, Pakistan

The recent energy shortfall in Pakistan has prompted the need for the development of hydropower projects to cope with the energy and monetary crisis. Hydropower in the northern areas is available yet has not been explored too much. Focusing on the sustainable development goal (SDG) “Ensure access to affordable, reliable, sustainable and modern energy”, thirteen proposed sites were selected from upstream to downstream of the Hunza River for analysis. The head on all the proposed sites was determined by taking the elevation difference between the proposed turbine and the intake at all sites. The discharge on all proposed ungauged sites was determined using ArcGIS for watershed delineation and the area ratio method along with Soil Conservation Service–Curve Number (SCS-CN) by using gauged data of Hunza River provided by Water and Power Development Authority (WAPDA) Pakistan at Daniyor bridge Gilgit, Shimshal and the Passo tributaries of Hunza River. The Location Search Algorithm (LSA) approach used a multi-criteria decision-making tool (MDM) to make a decision matrix considering the location and constraint criteria and then normalizing the decision matrix using benefit and cost criteria, the relative weights were assigned to all criteria using a rank sum weighted method and the sites were ranked on the basis of the final score. The results revealed that Hunza River has a significant hydropower potential and based on the final score in the LSA approach, proposed site 13, site 4 and site 9 were concluded as the most promising sites among proposed alternatives. The proposed methodology could be an encouraging step for decision makers for future hydropower development in Pakistan.

Monthly streamflow forecasting for the Hunza River Basin using machine learning techniques

Abstract Streamflow forecasting is crucial for planning, designing, and managing water resources. Accurate streamflow forecasting is essential in developing water resource systems that are both technically and economically efficient. This study tested several machine learning techniques to estimate monthly streamflow data in the Hunza River Basin, Pakistan, using streamflow, precipitation, and air temperature data between 1985 and 2013. The techniques tested included adaptive boosting (AB), gradient boosting (GB), random forest (RF), and K-nearest neighbors (KNN). The models were developed using river discharge as the target variable, while air temperature and precipitation as the input variables. The model's performance was assessed via four statistical performance indicators namely root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The results obtained for RMSE, MSE, MAE, and R2 using AB, GB, RF, and KNN techniques are (16.8, 281, 6.53, and 0.998), (95.1, 9,047, 61.5, and 0.921), (126.8, 16,078, 74.6, and 0.859), and (219.9, 48,356, 146.3, and 0.775), respectively. The results indicate that AB outperforms GB, RF, and KNN in predicting monthly streamflow for the Hunza River Basin. Machine learning, particularly AB, offers a reliable approach for streamflow forecasting, aiding hazard and water management in the area.

Streamflow forecasting for the Hunza river basin using ANN, RNN, and ANFIS models

Abstract Streamflow forecasting is essential for planning, designing, and managing watershed systems. This research study investigates the use of artificial neural networks (ANN), recurrent neural networks (RNN), and adaptive neuro-fuzzy inference systems (ANFIS) for monthly streamflow forecasting in the Hunza River Basin of Pakistan. Different models were developed using precipitation, temperature, and discharge data. Two statistical performance indicators, i.e., root mean square error (RMSE) and coefficient of determination (R2), were used to assess the performance of machine learning techniques. Based on these performance indicators, the ANN model predicts monthly streamflow more accurately than the RNN and ANFIS models. To assess the performance of the ANN model, three architectures were used, namely 2-1-1, 2-2-1, and 2-3-1. The ANN architecture with a 2-3-1 configuration had higher R2 values of 0.9522 and 0.96998 for the training and testing phases, respectively. For each RNN architecture, three transfer functions were used, namely Tan-sig, Log-sig, and Purelin. The architecture with a 2-1-1 configuration based on tan-sig transfer function performed well in terms of R2 values, which were 0.7838 and 0.8439 for the training and testing phases, respectively. For the ANFIS model, the R2 values were 0.7023 and 0.7538 for both the training and testing phases, respectively. Overall, the findings suggest that the ANN model with a 2-3-1 architecture is the most effective for predicting monthly streamflow in the Hunza River Basin. This research can be helpful for planning, designing, and managing watershed systems, particularly in regions where streamflow forecasting is crucial for effective water resource management.

Assessment of Runoff Components of River Flow in the Karakoram Mountains, Pakistan, during 1995–2010

Glaciers are generally believed to be subjugating by global warming but the Karakoram glaciers are reportedly maintaining their balance. Earlier studies in the Karakoram and its sub-basins have mostly addressed a short span of time and used complex models to understand the phenomenon. Thus, this study is based on a long-term trend analysis of the computed runoff components using satellite data with continuous spatial and temporal coverage incorporated into a simple degree day Snowmelt Runoff Model (SRM). The trends of melt runoff components can help us understanding the future scenarios of the glaciers in the study area. The SRM was calibrated against the recorded river flows in the Hunza River Basin (HRB). Our simulations showed that runoff contribution from rain, snow, and glaciers are 14.4%, 34.2%, and 51.4%, respectively during 1995–2010. The melting during the summer has slightly increased, suggesting overall but modest glacier mass loss which consistent with a few recent studies. The annual stream flows showed a rising trend during the 1995–2010 period, while, rainfall and temperatures showed contrasting increasing/decreasing behavior in the July, August, and September months during the same period. The average decreasing temperatures (0.08 °C per annum) in July, August, and September makes it challenging and unclear to explain the reason for this rising trend of runoff but a rise in precipitation in the same months affirms the rise in basin flows. At times, the warmer rainwater over the snow and glacier surfaces also contributed to excessive melting. Moreover, the uncertainties in the recorded hydrological, meteorological, and remote sensing data due to low temporal and spatial resolution also portrayed contrasting results. Gradual climate change in the HRB can affect river flows in the near future, requiring effective water resource management to mitigate any adverse impacts. This study shows that assessment of long-term runoff components can be a good alternative to detect changes in melting glaciers with minimal field observations.

Assessment of glacier status and its controlling parameters from 1990 to 2018 of Hunza Basin, Western Karakorum.

Ice masses and snow of Hunza River Basin (HRB) are an important primary source of fresh water and lifeline for downstream inhabitants. Changing climatic conditions seriously put an impact on these available ice and snow masses. These glaciers may affect downstream population by glacial lake outburst floods (GLOF) and surge events due to climatic variation. So, monitoring of these glaciers and available ice masses is important. This research delivers an approach for dynamics of major glaciers of the Hunza River Basin. We delineated 27 major glaciers of HRB and examined their status by using Landsat (OLI, ETM+, ETM, TM), digital elevation model (DEM) over the period of 1990-2018. In 1990, the total area covered by these glaciers is about 2589.75 ± 86 km2 and about 2565.12 ± 68km2 in 2018. Our results revealed that from 2009 to 2015, glacier coverage of HRB advanced with a mean annual advance rate of 2.22 ± 0.1 km2 a-1. Conversely, from 1994 to 1999, the strongest reduction in glacier area with a mean rate of - 3.126 ± 0.3 km2 a-1 is recorded. The glaciers of HRB are relatively stable compared to Hindukush, Himalayan, and Tibetan Plateau region of the world. The steep slope glacier's retreat rate is more than that of gentle slope glaciers, and the glaciers below an elevation of 5000 m above sea level change significantly. Based on climate data from 1995 to 2018, HRB shows a decreasing trend in temperature and increasing precipitation. The glacier area's overall retreat is due to an increase in summer temperature while the glacier advancement is induced possibly by winter and autumn precipitation.

Inventory and GLOF Susceptibility of Glacial Lakes in Hunza River Basin, Western Karakorum

Northern latitudes of Pakistan are warming at faster rate as compared to the rest of the country. It has induced irregular and sudden glacier fluctuations leading to the progression of glacial lakes, and thus enhancing the risk of Glacier Lake Outbursts Floods (GLOF) in the mountain systems of Pakistan. Lack of up-to-date inventory, classification, and susceptibility profiles of glacier lakes and newly formed GLOFs, are few factors which pose huge hindrance towards disaster preparedness and risk reduction strategies in Pakistan. This study aims to bridge the existing gap in data and knowledge by exploiting satellite observations, and efforts are made to compile and update glacier lake inventories. GLOF susceptibility assessment is evaluated by using Analytical Hierarchy Process (AHP), a multicriteria structured technique based on three susceptibility contributing factors: Geographic, topographic, and climatic. A total of 294 glacial lakes are delineated with a total area of 7.85 ± 0.31 km2 for the year 2018. Analysis has identified six glacier lakes as potential GLOF and met the pre-established criteria of damaging GLOFs. The historical background of earlier GLOF events is utilized to validate the anticipated approach and found this method appropriate for first order detection and prioritization of potential GLOFs in Northern Pakistan.

Rock glacier inventory, permafrost probability distribution modeling and associated hazards in the Hunza River Basin, Western Karakoram, Pakistan

The destabilization of rock glaciers and permafrost variations is of great importance to the safety of the population and infrastructure in the Karakoram region because of their effects on land stability and river obstructions. In this study, we compiled the first complete rock glacier inventory for the Hunza Basin, western Karakoram, of 616 rock glaciers with an area of 194 km2 between 2800 and 5700 m a.s.l. We categorized the rock glaciers as intact or relict, and their distributions and destabilization were further analyzed and used along with in situ climate and elevation dataset to model the permafrost probability distribution. The modeled areas where the permafrost zonation index (PZI) is 0.5–1.00 indicate that permafrost occurs over 85% of the catchment area and lies above 3525 m a.s.l., which closely matches the zero-degree isotherm of 3800 m a.s.l. Based on the sensitivity analysis of the independent variables, elevation is the most sensitive variable, followed by net radiation, for predicting the probabilities of the presence and absence of permafrost. The model distributions are quite precise, with median posterior areas under the curve of 0.98 and 0.96 for model training and testing, respectively. We analyzed the rock glacier destabilization for 68 rock glaciers that interacted with river channels, of which 50 blocked or diverted river channels. Destabilized rock glaciers can be closely linked to the 0 °C isotherm between 3400 and 4600 m a.s.l. The significant damage caused by periodic floods from the subsequent blockage of river channels by landslides can be attributed to variations in permafrost. Which demolished infrastructure, including a hydropower plant, suspension bridge and water supply system in Hassan-abad catchment. Quantification of rock glacier dynamics and permafrost in the region can further improve policies related to the reduction in disaster risk and mitigation of associated hazards.

Mapping Fluctuations of Hispar Glacier, Karakoram, using Normalized Difference Snow Index (NDSI) and Normalized Difference Principal Component Snow Index (NDSPCSI)

Investigation of the fluctuations in the snow-covered area of the major glaciers of the Karakoram range is essential for proper water resource management in Pakistan, since its glaciers are responding differently to the rising temperatures. The objective of this paper is to map snow covered area of Hispar glacier in Hunza river basin for the years 1990, 2010 and 2018. Two techniques, (NDPCSI) Normalized Difference Principal Component Snow Index and (NDSI) Normalized Difference Snow Index were used. Hispar glacier of the Hunza basin has lost 114 km2 of its ice cover area, during the last 28 years, with an alarming annual retreat rate of 1.67 km2 of glacier ice from 1990 to 2018. Hunza basin experienced a +1°C rise in both mean minimum and mean maximum temperature during 2007 to 2018.as a result, Karakorum ice reserves have been affected by rising temperature of the region. Due to temperature rise, retreat of snowcovered area of Hispar, Karakoram mountain range shows a shift in the cryospheric hazard zone.

Glacier-Related Hazards Along the International Karakoram Highway: Status and Future Perspectives

The China–Pakistan international Karakoram Highway passes through the core area of the “Karakoram Anomaly,” whose glaciers have maintained or increased their mass during a period when most glaciers worldwide have receded. We synthesized the literature and used remote-sensing techniques to review the types, distribution, characteristics, causes and frequency of major glacial hazards along the Karakoram Highway. We found that the glacier-related hazards could be divided into direct and indirect hazards, including glacier surges, glacial lake outburst floods, and glacial floods, which are concentrated in East Pamir and the Hunza River Basin. In the past 100 years, hazards from glaciers surges and glacial floods only occurred once and twice, respectively, which appear suddenly, with the hazard-causing process being short-lived and occurring mainly in the summer. Glacial lake outburst floods mainly occur in the spring and summer in the Hunza River Basin. Among these, ice-dammed lakes have the highest frequency of flooding, their formation and outbursts being closely related to the sudden advancement of surge-type glaciers. Under the background of global climate warming, we speculate that the glacier surge cycle may shorten and the frequency of the formation and outbursts in the glacial lakes may increase. In the future, we should combine models and new field observations to simulate, and deepen our understanding of the physical mechanisms of different glacier-related hazards. In particular, on-site monitoring should be carried out, to include the evolution of glaciers subglacial hydrological systems, the thermal state at the base of the glaciers, and the opening and closing of drainage channels at the base of the ice dams.

Agricultural water management challenges in the Hunza River Basin: Is a solar water pump an alternative option?*

AbstractIn the glaciated mountains of the Upper Indus Basin of Pakistan, glacier‐ and snowmelt‐ based irrigation systems have been established over several centuries to secure water for cultivation. However, these systems are now facing several challenges, being sensitive to climate change and thus exposed to the resulting extreme events such as glacial lake outburst floods (GLOFs) and flash floods, also difficult terrain and access paths, male outmigration, limited technical capacity and a weak policy and governance system. Hence, exploring the potential options for overcoming the challenges and irrigating arable barren land could lead to economic prosperity and environmental gains.A literature review, stakeholder consultation and semi‐structured questionnaire survey in two villages of the Hunza River basin was conducted to synthesize information in different domains. Information on the irrigation system and its sources, water distribution/allocation system, challenges and adaptation strategies were collected, and based on community preferences, an alternative irrigation technological package was established to overcome the challenges and irrigate barren land. A customized solar water lifting pump with an efficient micro‐irrigation package was piloted which proved to be climate resilient. The system is simple to adopt, uses clean energy for operation and is economically feasible, with a benefit–cost ratio of 4.96 and a payback period of around 11 years.

Geospatial Analysis and Simulation of Glacial Avalanche Hazard in Hunza River Basin

Glacial avalanche hazard poses threat to human lives and damage settlements / infrastructures in alpine glaciers mountainous regions. A gigantic ice plus rock avalanche destroyed Gyari military camp in Siachen sector on April 2012 and buried 139 personals. The study focuses on geospatial analysis and simulation of Shishper glacial avalanche of Hunza basin. To simulate the potential glacial avalanche hazard to Hassan Abad settlements, an empirical process based Glacier Avalanche Model; Rapid Access Mass Movement Simulation (RAMMS) is utilized. The model encompasses avalanche release area and height for the execution of simulation. The model output of Shishper glacial avalanche resulted; a max pressure of 450 Kpa, max velocity of 40 m/s, and the max flow height of 80m, while the resulted surge extent output was 2500m. The potential hazardous Shishper glacial avalanche remains a continuous hazard to Hassan Abad of Hunza valley including Karakoram Highway and Frontier Works Organization (FWO) camp. The study has resulted in identifying the Upper Indus Bain (UIB) being more prone to glacial avalanche hazards because of host factors in general and the anthropogenic factor in particular.

A brief communication of shispar glacier surge in 2018, hunza river basin, Pakistan

Glacier surging is a common phenomenon in the Karakoram region, but the driving mechanisms, their occurrence and its relation to a changing climate remain are unclear. In this study, we use Sentinel imagery to quantify advancement of the Shispar glacier during a surge in 2018. Results reveal that Shispar glacier starts rapid surging from Jun 2018. The peak surge is in August 2018. Our data reveal that glacier dammed the Hassanabad stream as result lake formation in upstream area and drainage of the lake also blocked. The surging is continuing and size of newly formed glacier lake is also increasing day by day. Currently, the inflow to lake is very low due to low melting (negative temperature) from the upstream glacier. This inflow of glacier meltwater will be an increase in the summer season, which may grow in lake size and could pose threats to downstream settlements and infrastructure (irrigation channels, powerhouses and bridge at Karakoram Highway (KKH) in the case of a sudden breach in the form of glacial lake outburst flood (GLOF). Currently the damages the powerhouse channel and damage the irrigation channel of Aliabad Hunza. This study recommended that there is a need for monitoring of glacier lake size and blockage area using remote sensing data i.e. satellite images and UAV.

Comprehensive evaluation of 0.25° precipitation datasets combined with MOD10A2 snow cover data in the ice-dominated river basins of Pakistan

A major portion of Pakistan's economy is based on cultivated lands which are irrigated from the supply of water from Upper Indus River Basins (UIB). Any change in UIB rivers flows may come with catastrophic events and therefore, will destructively affect Pakistan's economy. By aiming this scenario, an uneven and important climate variable (i.e., precipitation) obtained from different gridded and satellite datasets were used for its statistical and hydrological performance evaluation in UIB catchments for the period of 2000 to 2004. In addition, a bias corrected technique and snow cover product (MOD10A2) was also used to enhance the performance of precipitation data sets to obtain realistic discharge simulations. The results indicated that without correcting the biases from the datasets, only APHRODITE precipitation dataset showed higher correlation with observations compared to other precipitation datasets in Hunza River Basin (HRB) with correlation coefficient of (0.44) & and in Gilgit River Basin (GRB) (0.35), respectively. However, after applying bias correction technique (quantile mapping), the performance of precipitation datasets significantly improved. For GRB, correlation coefficient and root mean square values improved up to 48% & 55%, while for HRB up to 53% & 51%, respectively. Likewise, based on hydrological utility which was implied by the well-known hydrological model (snowmelt runoff model), bias corrected CHIRPS and APHRODITE precipitation datasets displayed best performance in simulating the discharge with Nash–Sutcliffe coefficient (0.82 & 0.90) & correlation coefficient (0.83 & 0.84) in HRB and (0.84 & 0.80) and (0.86 & 0.82) in GRB, respectively. Moreover, recalibration was also carried out to assess how the hydrological model can adjust and tolerate the errors of different precipitation data products. The results revealed that after adjusting the model parameters particularly coefficient of rainfall and coefficient of snow, the performance of data products significantly improved in terms of the difference in volumes against in situ measurements. Overall, this study may assist, provide guidelines and efficiently used for snowmelt runoff model coupled with different precipitation datasets for management of Indus River irrigation system of Pakistan.

Water Balance Assessment under Different Glacier Coverage Scenarios in the Hunza Basin

The potential impact of glacier recession on river discharge from the Hunza river basin was estimated as an indicator for downstream changes in the Indus river system. The J2000 model was used to analyze the water balance in the basin and simulate the contribution of snow and ice melt to total discharge at present and under three scenarios of glacier recession. Precipitation was corrected using virtual weather stations created at a higher elevation and a precipitation gradient. Snowmelt from the whole basin contributed, on average, 45% of the total river discharge during the modeling period and 47% of the ice melt from the glacier area. Total ice melt declined by 55%, 81%, and 96% under scenarios of glacier recession to 4000, 4500, and 5000 masl, respectively. The contribution of ice melt to river discharge decreased to 29%, 14%, and 4% under the three scenarios, while total discharge from the Hunza river decreased by 28%, 40%, and 46%. The results suggest that glacier recession in the Hunza river basin could have serious implications for downstream water availability. Understanding melt contribution in the basin based on ongoing and projected future climatic change can play a crucial role in future water resource management.

Projection of future streamflow of the Hunza River Basin, Karakoram Range (Pakistan) using HBV hydrological model

Hydrologiska Byrans Vattenbalansavdeling (HBV) Light model was used to evaluate the performance of the model in response to climate change in the snowy and glaciated catchment area of Hunza River Basin. The study aimed to understand the temporal variation of streamflow of Hunza River and its contribution to Indus River System (IRS). HBV model performed fairly well both during calibration (R2=0.87, Reff=0.85, PBIAS=−0.36) and validation (R2=0.86, Reff=0.83, PBIAS=−13.58) periods on daily time scale in the Hunza River Basin. Model performed better on monthly time scale with slightly underestimated low flows period during both calibration (R2=0.94, Reff=0.88, PBIAS=0.47) and validation (R2=0.92, Reff=0.85, PBIAS=15.83) periods. Simulated streamflow analysis from 1995–2010 unveiled that the average percentage contribution of snow, rain and glacier melt to the streamflow of Hunza River is about 16.5%, 19.4% and 64% respectively. In addition, the HBV-Light model performance was also evaluated for prediction of future streamflow in the Hunza River using future projected data of three General Circulation Model (GCMs) i.e. BCC-CSM1.1, CanESM2, and MIROCESM under RCP2.6, 4.5 and 8.5 and predictions were made over three time periods, 2010–2039, 2040–2069 and 2070–2099, using 1980–2010 as the control period. Overall projected climate results reveal that temperature and precipitation are the most sensitive parameters to the streamflow of Hunza River. MIROC-ESM predicted the highest increase in the future streamflow of the Hunza River due to increase in temperature and precipitation under RCP4.5 and 8.5 scenarios from 2010–2099 while predicted slight increase in the streamflow under RCP2.6 during the start and end of the 21th century. However, BCCCSM1.1 predicted decrease in the streamflow under RCP8.5 due to decrease in temperature and precipitation from 2010–2099. However, CanESM2 predicted 22%-88% increase in the streamflow under RCP4.5 from 2010–2099. The results of this study could be useful for decision making and effective future strategic plans for water management and their sustainability in the region.

Hypsometric properties of mountain landscape of Hunza River Basin of the Karakoram Himalaya

Within Karakoram Himalaya, Hunza River Basin (study area) is unique for a number of reasons: 1) potential impacts of highly concentrated high-pitched mountains and glacial ice; 2) the glaciated portions have higher mean altitude as compared to other glaciated landscapes in the Karakoram; 3) this basin occupies varieties of both clean and debris-covered glaciers and/or ice. Therefore, it is imperative to understand the stability of topographic surface and potential implications of fluctuating glacial-ice causing variations in the movement of material from higher to lower elevations. This paper advocates landscape-level hypsometric investigations of glaciated landscape lies between 2280–7850 m elevation above sea level and non-glaciated landscape between 1461–7570 m. An attempt is made to understand intermediate elevations, which disguise the characteristics of glaciated hypsometries that are highly correlated with the Equilibrium Line Altitude (ELA). However, due to data scarcity for high altitude regions especially above 5000 m elevation, literature values for climatic conditions are used to create a relationship between hypsometry and variations in climate and ELA. The largest glaciated area (29.22%) between 5047 to 5555 m lies in the vertical regime of direct snow-accumulation zone and in the horizontal regime of net-accumulation zone (low velocity, net freezing, and no-sliding). In both landscapes, the hypsometric curves are ‘slow beginning’ followed by ‘steep progress’ and finally reaching a ‘plateau’, reflecting the rapid altitudinal changes and the dominance of fluvial transport resulting in the denudation of land-dwelling and the transport of rock/debris from higher to lower altitudes. Reported slight differences in the average normalized bin altitudes against the cumulative normalized area between glaciated and non-glaciated landscapes are an indicator of slightly different land-forms and landform changes.

Investigating Snow Cover and Hydrometeorological Trends in Contrasting Hydrological Regimes of the Upper Indus Basin

The Upper Indus basin (UIB) is characterized by contrasting hydrometeorological behaviors; therefore, it has become pertinent to understand hydrometeorological trends at the sub-watershed level. Many studies have investigated the snow cover and hydrometeorological modeling at basin level but none have reported the spatial variability of trends and their magnitude at a sub-basin level. This study was conducted to analyze the trends in the contrasting hydrological regimes of the snow and glacier-fed river catchments of the Hunza and Astore sub-basins of the UIB. Mann-Kendall and Sen’s slope methods were used to study the main trends and their magnitude using MODIS snow cover information (2001–2015) and hydrometeorological data. The results showed that in the Hunza basin, the river discharge and temperature were significantly (p ≤ 0.05) decreased with a Sen’s slope value of −2.541 m3·s−1·year−1 and −0.034 °C·year−1, respectively, while precipitation data showed a non-significant (p ≥ 0.05) increasing trend with a Sen’s slope value of 0.023 mm·year−1. In the Astore basin, the river discharge and precipitation are increasing significantly (p ≤ 0.05) with a Sen’s slope value of 1.039 m3·s−1·year−1 and 0.192 mm·year−1, respectively. The snow cover analysis results suggest that the Western Himalayas (the Astore basin) had a stable trend with a Sen’s slope of 0.07% year−1 and the Central Karakoram region (the Hunza River basin) shows a slightly increasing trend with a Sen’s slope of 0.394% year−1. Based on the results of this study it can be concluded that since both sub-basins are influenced by different climatological systems (monsoon and westerly), the results of those studies that treat the Upper Indus basin as one unit in hydrometeorological modeling should be used with caution. Furthermore, it is suggested that similar studies at the sub-basin level of the UIB will help in a better understanding of the Karakoram anomaly.

Spatio‐temporal analysis of glacial ice area distribution of Hunza River Basin, Karakoram region of Pakistan

AbstractThere is a high degree of uncertainty about the state and fate of Pakistan's Karakoram glaciers due to data scarcity in high altitude regions. They are thought to be less vulnerable to climatic change because they behave differently as compared with eastern Himalayas. This study measures the decadal temporal changes in the glacial ice area of Karakoram's Hunza River Basin, one of the eight subbasins of Upper Indus Basin. An attempt has been made to investigate the relationship between glacial ice area changes and calculated values of precipitation, temperature and run‐off. A combination of satellite and field‐based approach is applied. Output includes maps of glacial ice hypsometries of eight glacial ice subregions of Hunza River Basin for 3 years (i.e., 1989, 2002, and 2010). The results show a decreasing trend in the glacial ice‐covered area signifying a reduction of 20.47% with the largest reduction being in the lower elevation bands. There is presently no conclusive answer as to why glacial ice in the Karakoram is acting differently from the near‐global indication of glacial ice changes. Climate data from high altitudes are needed to find answer for this anomalous behaviour.

Spatial variation, source identification, and quality assessment of surface water geochemical composition in the Indus River Basin, Pakistan.

The Indus River Basin (IRB) with an area of 139,202km2 is the lifeline river basin of Pakistan. An intensive study was conducted in six subcatchments of the IRB with five in the Upper Indus Basin (UIB) and one of the Lower Indus Basin (LIB; between Tarbela Dam and Panjand), i.e., the Gilgit River Basin (UIB-I), Hunza River Basin (UIB-II), UIB-III, UIB-IV and UIB-V, and LIB. A total of 84 surface water samples were collected from main stream and tributaries from June to August, 2016. The pH, electric conductivity (EC), and total dissolved solids (TDS) were measured in situ, whereas major ions (Ca2+, Mg2+, K+, Na+, Cl-, SO42-, and NO3-) and Si were analyzed in the laboratory. The results exhibited alkaline pH (8.55 ± 0.20) with diverse TDS (114.69 ± 77.65mgL-1) and ion concentrations that were characterized primarily by the Ca-Mg-HCO3 type in the whole basin. The average TDS in the UIB and LIB were 93.99 ± 39.73 and 181.67 ± 167.82mgL-1, respectively, under the influence of the arid to semi-arid climatic conditions and relatively higher anthropogenic interference in LIB compared to the UIB. The order of dominant major cations was different in the UIB and LIB, reflecting the diverse nature of geological formation. Gibbs plot, mixing diagrams, and ionic ratios were used to identify the controlling mechanism of river geochemistry in the IRB as carbonate weathering in general with different degrees of silicate weathering and minor contribution by evaporite dissolution. In addition, principal component/factor analysis also indicated that the major sources of dissolved loads in the basin are carbonates followed by silicates. Significant influences of silicate minerals were observed in the LIB, and there was a large contribution of evaporites in the UIB-II, UIB-III, and LIB. The suitability assessment showed that the river water fits the WHO permissible limits for drinking purposes from the perspective of major ions, whereas for irrigation purposes, most of the samples exhibited excellent and good levels except for a couple of permissible and doubtful levels from the Sawan and Deratang tributaries in the LIB, which may deteriorate the quality of soil and degrade the water quality downstream.