Hunza River Research Articles

Spatio-Temporal Monitoring and Risk Mapping of Glacial Lake Outburst Flood in Hunza Valley, Pakistan

Glacial lake outburst flood (GLOF) disasters are serious and potentially increase huge risks to livelihoods and infrastructure in the mountain regions of the world. The northern highland regions of Pakistan are home to some of the biggest alpine glaciers. In this investigation, the Hunza Valley of Pakistan has undergone remote sensing-based risk assessment for Glacial Lake Outburst Floods. Borith and Passu Lakes were chosen to identify flood risk in the downstream areas. For such, Landsat images were used from 1990-2020. Different spectral indices such as the Normalized Difference Snow Index (NDSI), Normalized Difference Glacier Index (NDGI) and Normalized Difference Water Index (NDWI) were applied to evaluate snow cover changes. Furthermore, these Lakes were digitized to evaluate and check the total increase in Lake Areas. Also, built-up areas close to lakes were digitized to identify total risk. The Land Surface Temperature (LST), NDSI, NDGI, NDWI, Digital Elevation Model (DEM) and Slope variables were given weights using the Analytic Hierarchy Process (AHP) method. In the analysis of flood risk mapping, maximum weight was assigned to Land Surface Temperature, and minimum weight was assigned to the slope. The result revealed that the settlements located in the Ghulkin, Gulmit, Husseini, Passu, Zarabad, and Khorramabad are at moderate risk while settlements located near Hunza River such as Karimabad, Khanna Abad, and Aliabad are at high risk. The outcome also showed that Borith Lake's area expanded, going from 0.059 km2 in 1985 to 0.074 km2 in 2020 and Passu Lake's area also grew, going from 0.074 km2 in 2005 to 0.077 km2 in 2020. In last, Buffer analysis was performed to identify areas that are likely to be affected by the flood. The result of the study can help carry out a downstream risk assessment and better preparedness for future flood hazards.

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.

Assessing long-term variability and trends in temperature and precipitation in Gilgit and Hunza river basins

Assessing long-term variability and trends in temperature and precipitation in Gilgit and Hunza river basins

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.

Impact of Climate Change on the Stability of the Miacher Slope, Upper Hunza, Gilgit Baltistan, Pakistan

Especially in recent years, the study of landslide phenomena is considered as very important because of the effects of climate change. The aim of this paper is to examine the stability of the slope located in Miacher Nagar village along the Hunza River (HR), using the Limit Equilibrium Method (LEM). The Miacher slope rises to a height of 900 m from the foot of the Hunza River and has a base angle of 50 degrees. Meta-sediments and quaternary recent glaciated deposits make up the majority of the slope’s composition. The slope movement of Miacher was first triggered in 1995, and was further triggered in 2010 and 2013. The slope was geologically, geomorphologically, geotechnically and geochemically investigated as well as modeled by Slope/w to determine the safety factor. Soil samples were analyzed for their geotechnical, geological and geomorphological properties. The Limit Equilibrium Method (LEM) was employed in this study to analyze the Factor of Safety (FOS) of the slope, based on assumptions of the Morgenstern and Price, Ordinary, Janbu and Bishop Methods, using the Slope/w software. Various factors, including pore water pressure, unit weight, cohesion, angle of internal friction and overburden, were examined by analyzing different scenarios. The findings showed that an increase in cohesion and angle of internal friction resulted in an increase in FOS, whereas an increase in unit weight and overburden caused a decrease in FOS. The influence of pore water pressure was positive to a certain extent, but a further increase led to a significant reduction in FOS. The results showed that the Miacher slope is currently stable, as all FOS values were greater than one, based on the existing strength parameters and simulated results obtained using Slope/w.

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.

SLOPE STABILITY ANALYSIS AND MITIGATION ALONG CPEC ROUTE (KKH) BY USING ROCSCIENCE SLIDE MODELING;(A CASE STUDY OF LANDSLIDE AT HUMARRI, PAKISTAN

Northern Pakistan is one of the most active and dangerous geological zones on the planet. As a result, several huge landslides have occurred in the area throughout history, destroying infrastructure, blocking the Hunza River, and seriously damaging the Karakoram highway. Due to the difficult logistics and wide distances involved, despite the high frequency of large-magnitude landslides and the subsequent destruction, the whole area remains understudied. Using the Limit Equilibrium Method, this study explores the slope failure along the Hunza River (HR), Village Humarri Nagar District Upper Hunza Gilgit Baltistan, Pakistan (LEM). Using Slide Rocscience programmer, the slope was fully studied for geological conditions, slope geometry, soil strength parameters, and FOS determination. Soil samples from the slope were gathered for analysis of geotechnical properties, and the slope's geometry was also analysed during the excursion. Moisture content (w) 1%, specific gravity (Gs) 2.64, unit weight (r) 19kN/m3, angle of internal friction (AIF) 30 and cohesion (c) 69 kPa are the strength parameters of the soil sample. The Humarri active landslide soil was classed as sandy silt with the group sign ML by the Unified Classification System (UCS). In Slide Rocscience programme, the FOS of the slope was calculated using LEM with the assumption of ordinary (O), Janbu (J), and Bishop (B) techniques. A set of instances were used to calculate the influence of Pore Water Pressure (PWP), Unit Weight, Cohesion, Angle of Internal Friction, and Overburden on FOS. FOS increases when cohesiveness and angle of internal friction rise; yet, FOS increases as unit weight and overburden of overlying materials grow. Furthermore, while PWP has some good effects, increasing it causes a significant decline in FOS. Because every FOS is larger than 1, the Humarri active landslide is judged to be stable in its current form based on available strength metrics and simulated slide conscience results.

Contemporary Trends in High and Low River Flows in Upper Indus Basin, Pakistan

The Upper Indus Basin (UIB) features the high mountain ranges of the Hindukush, Karakoram and Himalaya (HKH). The snow and glacier meltwater contribution feeds 10 major river basins downstream including Astore, Gilgit, Hunza, Jhelum, Kabul, Shyok and Shigar. Climate change is likely to fluctuate the runoff generated from such river basins concerning high and low streamflows. Widening the lens of focus, the present study examines the magnitude and timing of high flows variability as well as trends variability in low streamflows using Sen’s slope and the Mann-Kendall test in UIB from 1981 to 2016. The results revealed that the trend in the magnitude of the high flows decreased at most of the sub-basins including the Jhelum, Indus and Kabul River basins. Significantly increased high flows were observed in the glacier regime of UIB at Shigar and Shyok while decreased flows were predominant in Hunza River at Daniyor Bridge. A similar proclivity of predominantly reduced flows was observed in nival and rainfall regimes in terms of significant negative trends in the Jhelum, Kunhar, Neelum and Poonch River basins. The timing of the high flows has not changed radically as magnitude at all gauging stations. For the low flows, decreasing significant trends were detected in the annual flows as well as in other extremes of low flows (1-day, 7-day, 15-day). The more profound and decreasing pattern of low flows was observed in summer at most of the gauging stations; however, such stations exhibited increased low flows in autumn, winter and spring. The decrease in low flows indicates the extension of dry periods particularly in summer. The high-water demand in summer will be compromised due to consistently reducing summer flows; the lower the water availability, the lower will be the crop yield and electricity generation.

Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan

Abstract Floods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested for classification: classical algorithms (Random Forest, RF and Support Vector Classifier, SVC) and deep learning algorithms (Multi-Layer Perceptron, MLP). For the regression problem, the result of MLP and Support Vector Regression (SVR) algorithms were compared based on their mean square, root mean square and mean absolute errors. The results obtained show that the accuracy of the RF classifier is 0.99, while the accuracies of the SVC and MLP methods are 0.98; moreover, in the case of flood prediction, the SVR algorithm outperforms the MLP approach.

Initial Screening of Regional Landslide Hazards in the Hunza River Watershed, Gilgit Baltistan, Pakistan

AbstractLandslides pose a significant hazard to life and property in areas vulnerable to mass movements. Qualitative landslide susceptibility maps offer a preliminary means of screening locations l...

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.

A comparative analysis of attabad landslide on january 4, 2010, using two numerical models

This study determines the runout behavior (Attabad landslide, Hunza, Pakistan) of one of the biggest landslides in Pakistan's history, along the highest and strategically most important highway of the world. On January 4, 2010, at 08:36 local time, 45 Mm3 of rock mass flowed down the hill slope for 1060 m and fell in Hunza River, thus blocking the river's flow and making an artificial dam. This paper examines the landslide's failure process based on seismic data obtained from a published paper. The average velocity of the slide was found to be 14.32 m/s. To better understand the landslide dynamics and failure phenomena, a numerical simulation was conducted using DAN3D to simulate displaced materials' runout behavior. Simulation results indicate that the slope's failure lasted for 70 s, which is in good agreement with seismic wave recordings of 70 s. The combined frictional–Voellmy model obtained the most accurate results for simulation. Further, to verify the results, another simulation was run using RAMMS debris flow software; it was found that the results of both the software's are in good agreement. It is expected that the selected model and its parameters will help understand similar kind of rock avalanches in the area, which will help concerned agencies improve landslide prediction along Karakoram Highway.

Appraisal of a Running Glacier of Pakistan in Context of Geological Perspectives

Shishper glacier is surge type glacier which gave rise to Glacier Lake Outbursts Flood (GLOF) and an ice dammed lake. The probability of GLOF events has been increased in Pakistan’s mountain system due to increased temperature and irregular glacial fluctuations in northern region of Pakistan. The average rise of temperature in Pakistan is 1.04 ?C from the year 1960 to 2014. Rising temperature is initiating the recession of glaciers over the last decade which is indicating towards the evolution of glacial lakes in Basin of Hunza River. The Shishper glacier has travelled 800m during six months and about 1400m in the next six months in the year 2018. Shishper glacier has created a danger to fault lines and infrastructure of downstream of Hassanabad valley situated just below the hill. It travelled about 2.2km during 12 months. Temporal satellite imagery was used to evaluate susceptibility of GLOF events. Digital Elevation model was used to evaluate drainage patterns of Shishper glacier. Geological maps evaluated the geo-refred fault lines in the mountainous regions of Pakistan.

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.