Search Results (13)

The water regime in Pakistan’s northern region has experienced significant changes regarding hydrological extremes like floods because of climate change. Coupling hydrological models with remote sensing data can be valuable for flow simulation in data-scarce regions. This study focused on simulating the snow- and glacier-melt runoff using the snowmelt runoff model (SRM) in the Gilgit and Kachura River Basins of the upper Indus basin (UIB). The SRM was applied by coupling it with in situ and improved cloud-free MODIS snow and glacier composite satellite data (MOYDGL06) to simulate the flow under current and future climate scenarios. The SRM showed significant results: the Nash–Sutcliffe coefficient (NSE) for the calibration and validation period was between 0.93 and 0.97, and the difference in volume (between the simulated and observed flow) was in the range of −1.5 to 2.8% for both catchments. The flow tends to increase by 0.3–10.8% for both regions (with a higher increase in Gilgit) under mid- and late-21st-century climate scenarios. The Gilgit Basin’s higher hydrological sensitivity to climate change, compared to the Kachura Basin, stems from its lower mean elevation, seasonal snow dominance, and greater temperature-induced melt exposure. This study concludes that the simple temperature-based models, such as the SRM, coupled with improved satellite snow cover data, are reliable in simulating the current and future flows from the data-scarce mountainous catchments of Pakistan. The outcomes are valuable and can be used to anticipate and lessen any threat of flooding to the local community and the environment under the changing climate. This study may support flood assessment and mapping models in future flood risk reduction plans. Full article

Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their hybrid combinations (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Our research measured the model’s accuracy through root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R²). The findings indicated that the hybrid models, especially CNN-BiGRU and CNN-BiLSTM, achieved much better performance than traditional models like LSTM and GRU. For instance, CNN-BiGRU achieved the lowest RMSE (71.6 in training and 95.7 in testing) and the highest R² (0.962 in training and 0.929 in testing). A novel aspect of this research was the integration of MODIS-derived snow-covered area (SCA) data, which enhanced model accuracy substantially. When SCA data were included, the CNN-BiLSTM model’s RMSE improved from 83.6 to 71.6 during training and from 108.6 to 95.7 during testing. In peak streamflow prediction, CNN-BiGRU outperformed other models with the lowest absolute error (108.4), followed by CNN-BiLSTM (144.1). This study’s results reinforce the notion that combining CNN’s spatial feature extraction capabilities with the temporal dependencies captured by LSTM or GRU significantly enhances model accuracy. The demonstrated improvements in prediction accuracy, especially for extreme events, highlight the potential for these models to support more informed decision-making in flood risk management and water allocation. Full article

Headwater in the Indus River in Pakistan is largely dependent on the glaciers located in the northern part of the country, along with other sources such as direct precipitation. Glaciers are a major source of freshwater that provides agriculture and livelihood to millions of people. The hydro-climatic variations in the Gilgit watershed of the Upper Indus basin are poorly investigated scientifically due to high topographical differences, geography, remoteness of the region, and larger variations in climatic conditions. These glaciers are continuously changing due to melting as a consequence of global warming or accumulation due to snowfall/precipitation at higher altitude regions. The study is carried out using remote sensing data to quantify glacier changes in spatiotemporal variability in the past three decades. Five glaciers in the Gilgit region (near the junction of the Hindukush and Karakoram Mountains) with an area of more than 5 square kilometers were selected, namely Phakor, Karamber, East Gammu, Bhort, and Bad-e-Swat glaciers. These glaciers were monitored for changes in their sizes through a cloud-free continuous series of Landsat satellite imagery. The annual climatic trends were studied through spatially interpolated gridded climate data WοrldClim version-1 climate database for 1970–2000, utilized for assessment of meteorological condition by analyzing the variations of minimum and maximum temperature, solar radiation, and precipitation. The temporal variations in five glaciers in the Gilgit watershed are found to be minimal and, thus, are rather stable and show no sign of rapid melting or diminishing. The little variability of glaciers’ extent may be attributed to their geographic condition, altitude, topography, and orientation. The mapped glacier classes have been validated to check the accuracy assessment through an error matrix method. The kappa coefficient from the error matrix has been calculated as 84%, which shows a good agreement. The study makes a critical input towards understanding the dynamics of the glacier in the upper Indus catchment’s Gilgit watershed. Full article

Reliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The present study checked the applicability of the radial M5 tree (RM5Tree) model to accurately estimate sediment yields using daily inputs of the snow cover fraction, air temperature, evapotranspiration and effective rainfall, in addition to the flow, in the Gilgit River, Upper Indus Basin (UIB) tributary, Pakistan. The results of the RM5Tree model were compared with support vector regression (SVR), artificial neural network (ANN), multivariate adaptive regression spline (MARS), M5Tree, sediment rating curve (SRC) and response surface method (RSM) models. The resulting accuracy of the models was assessed using Pearson’s correlation coefficient (R²), the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE). The prediction accuracy of the RM5Tree model during the testing period was superior to the ANN, MARS, SVR, M5Tree, RSM and SRC models with the R², RMSE and MAPE being 0.72, 0.51 tons/day and 11.99%, respectively. The RM5Tree model predicted suspended sediment peaks better, with 84.10% relative accuracy, in comparison to the MARS, ANN, SVR, M5Tree, RSM and SRC models, with 80.62, 77.86, 81.90, 80.20, 74.58 and 62.49% relative accuracies, respectively. Full article

Glaciers in the Upper Indus Basin (UIB) in Pakistan are the major source of water, irrigation, and power production for downstream regions. Global warming has induced a substantial impact on these glaciers. In the present study, Landsat images were utilized to evaluate the glaciers for the period from 1990–2020 in the Central Karakoram National Park (CKNP) region to further correlate with climate parameters. The results reveal that glaciers are retreating and the highest (2.33 km²) and lowest (0.18 km²) recession rates were observed for Biafo and Khurdopin glaciers, respectively. However, a minor advancement has also been observed for the period from 1990–2001. More than 80% of glacier recession was recorded between 2009–2020 because mean summer temperature increased at both Skardu and Gilgit meteorological stations, while precipitation decreased at both stations from 2005–2020. The increase in mean summer temperature and decrease in winter precipitation resulted in glacial retreat, which will lead to water scarcity in the future as well as affect the agriculture sector and hydropower production in downstream areas of the Indus River basin. Full article

The Hindukush-Karakoram-Himalayan (HKH) ranges and their massive cryosphere extend over the Upper Indus Basin (UIB) and are prone to incapacitated water supply due to the proclivity of globally increased temperature. Due to excessive carbon emissions, frequent incursions including extreme climatic events, are likely to happen sooner than expected on a regional scale due to recent climate change. The present study examined the variability of climatic extremes (18 indices) during 1971 to2018 over the UIB. The Mann-Kendall (MK) test and Sen’s methods were applied for statistical analysis as the former deals with the magnitude of trends while the direction of observed trends was identified by the latter in climatological time-series data. The frequency and intensity of summer days (SU25 > 25 °C/year) at 13 out of 27 stations significantly increased, particularly in lower regions. The same warming proclivity was dominant in tropical nights (TR20 > 20 °C/year) at 20 stations including Astore, Bunji, Gilgit, Gupis, Murree and Skardu. Similarly, significant increases were observed in extremes of annual precipitation in western and high northern areas; however, significantly, the highest drops in R25 and R5day were exhibited in Chitral at the rates of 13 and 29 days, respectively. These findings tend to support the accelerated summer warming and a rather stable winter warming while stable winter warming showed that overall the UIB seems to be more sensitive towards warming. Full article

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. Full article

In contrast to widespread glacier retreat evidenced globally, glaciers in the Karakoram region have exhibited positive mass balances and general glacier stability over the past decade. Snow and glacier meltwater from the Karakoram and the western Himalayas, which supplies the Indus River Basin, provide an essential source of water to more than 215 million people, either directly, as potable water, or indirectly, through hydroelectric generation and irrigation for crops. This study focuses on water resources in the Upper Indus Basin (UIB) which combines the ranges of the Hindukush, Karakoram and Himalaya (HKH). Specifically, we focus on the Gilgit River Basin (GRB) to inform more sustainable water use policy at the sub-basin scale. We employ two degree-day approaches, the Spatial Processes in Hydrology (SPHY) and Snowmelt Runoff Model (SRM), to simulate runoff in the GRB during 2001–2012. The performance of SRM was poor during July and August, the period when glacier melt contribution typically dominates runoff. Consequently, SPHY outperformed SRM, likely attributable to SPHY’s ability to discriminate between glacier, snow, and rainfall contributions to runoff during the ablation period. The average simulated runoff revealed the prevalent snowmelt contribution as 62%, followed by the glacier melt 28% and rainfall 10% in GRB. We also assessed the potential impact of climate change on future water resources, based on two Representative Concentration Pathways (RCP) (RCP 4.5 and RCP 8.5). We estimate that summer flows are projected to increase by between 5.6% and 19.8% due to increased temperatures of between 0.7 and 2.6 °C over the period 2039–2070. If realized, increased summer flows in the region could prove beneficial for a range of sectors, but only over the short to medium term and if not associated with extreme events. Long-term projections indicate declining water resources in the region in terms of snow and glacier melt. Full article

The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R² value of 0.85 and 0.74 during the training and testing period, respectively. Full article

The China–Pakistan Economic Corridor (CPEC), a key hub for trade, is susceptible to glacial lake outburst floods. The distributions and types of glacial lakes in the CPEC are not well documented. In this study, cloud-free imagery acquired using the Landsat 8 Operational Land Imager during 2016–2018 was used to delineate the extent of glacial lakes in the mountainous terrain of the CPEC. In the study domain, 1341 glacial lakes (size ≥ 0.01 km²) with a total area of 109.76 ± 9.82 km² were delineated through the normalized difference water index threshold method, slope analysis, and a manual rectification process. On the basis of the formation mechanisms and characteristics of glacial lakes, four major classes and eight subclasses of lakes were identified. In all, 492 blocked lakes (162 end moraine-dammed lakes, 17 lateral moraine-dammed lakes, 312 other moraine-dammed lakes, and 1 ice-blocked lake), 723 erosion lakes (123 cirque lakes and 600 other erosion lakes), 86 supraglacial lakes, and 40 other glacial lakes were identified. All lakes were distributed between 2220 and 5119 m a.s.l. At higher latitudes, the predominate lake type changed from moraine-related to erosion. From among the Gez, Taxkorgan, Hunza, Gilgit, and Indus basins, most glacial lakes were located in the Indus Basin. The number and area of glacial lakes were larger on the southern slopes of the Karakoram range. Full article

Extensive research of the variability of flows under the impact of climate change has been conducted for the Upper Indus Basin (UIB). However, limited literature is available on the spatial distribution and trends of suspended sediment concentrations (SSC) in the sub-basins of UIB. This study covers the comparative assessment of flows and SSC trends measured at 13 stations in the UIB along with the variability of precipitation and temperatures possibly due to climate change for the past three decades. In the course of this period, the country’s largest reservoir, Tarbela, on the Indus River was depleted rapidly due to heavy sediment influx from the UIB. Sediment management of existing storage and future planned hydraulic structures (to tap 30,000 MW in the region) depends on the correct assessment of SSC, their variation patterns, and trends. In this study, the SSC trends are determined along with trends of discharges, precipitation, and temperatures using the non-parametric Mann–Kendall test and Sen’s slope estimator. The results reveal that the annual flows and SSC are in a balanced state for the Indus River at Besham Qila, whereas the SSC are significantly reduced ranging from 18.56%–28.20% per decade in the rivers of Gilgit at Alam Bridge, Indus at Kachura, and Brandu at Daggar. The SSC significantly increase ranging from 20.08%–40.72% per decade in the winter together with a significant increase of average air temperature. During summers, the SSC are decreased significantly ranging from 18.63%–27.79% per decade along with flows in the Hindukush and Western–Karakorum regions, which is partly due to the Karakorum climate anomaly, and in rainfall-dominated basins due to rainfall reduction. In Himalayan regions, the SSC are generally increased slightly during summers. These findings will be helpful for understanding the sediment trends associated with flow, precipitation, and temperature variations, and may be used for the operational management of current reservoirs and the design of several hydroelectric power plants that are planned for construction in the UIB. Full article

The Indus River, which flows through China, India, and Pakistan, is mainly fed by melting snow and glaciers that are spread across the Hindukush–Karakoram–Himalaya Mountains. The downstream population of the Indus Plain heavily relies on this water resource for drinking, irrigation, and hydropower generation. Therefore, its river runoff variability must be properly monitored. Gilgit Basin, the northwestern part of the Upper Indus Basin, is selected for studying cryosphere dynamics and its implications on river runoff. In this study, 8-day snow products (MOD10A2) of moderate resolution imaging spectroradiometer, from 2001 to 2015 are selected to access the snow-covered area (SCA) in the catchment. A non-parametric Mann–Kendall test and Sen’s slope are calculated to assess whether a significant trend exists in the SCA time series data. Then, data from ground observatories for 1995–2013 are analyzed to demonstrate annual and seasonal signals in air temperature and precipitation. Results indicate that the annual and seasonal mean of SCA show a non-significant decreasing trend, but the autumn season shows a statistically significant decreasing SCA with a slope of −198.36 km²/year. The annual mean temperature and precipitation show an increasing trend with highest values of slope 0.05 °C/year and 14.98 mm/year, respectively. Furthermore, Pearson correlation coefficients are calculated for the hydro-meteorological data to demonstrate any possible relationship. The SCA is affirmed to have a highly negative correlation with mean temperature and runoff. Meanwhile, SCA has a very weak relation with precipitation data. The Pearson correlation coefficient between SCA and runoff is −0.82, which confirms that the Gilgit River runoff largely depends on the melting of snow cover rather than direct precipitation. The study indicates that the SCA slightly decreased for the study period, which depicts a possible impact of global warming on this mountainous region. Full article

Pakistan is currently facing physical and economic water scarcity issues that are further complicated by the rapid increase in its population and by climate change. Many studies have focused on the physical water scarcity using hydrological modeling and the measurement of the impact of climate change on water resources in the Upper Indus Basin (UIB). However, few studies have concentrated on the importance of the economic water scarcity, that is, the water management issue under the looming impacts of climate change and the population explosion of Pakistan. The purpose of this study is to develop a management strategy which helps to achieve water security and sustainability in the Upper Indus Basin (UIB) with the help of different socio-economic and climate change scenarios using WEAP (Water Evaluation and Planning) modeling. The streamflow data of five sub-basins (Gilgit, Hunza, Shigar, Shyok, and Astore) and the entire Upper Indus Basin (UIB) were calibrated (2006–2010) and validated (2011–2014) in the WEAP model. The coefficient of determination and Nash Sutcliffe values for the calibration period ranged from 0.81–0.96. The coefficient of determination and the Nash Sutcliffe values for the validation period ranged from 0.85–0.94. After the development of the WEAP model, the analysis of the unmet water demand and percent coverage of the water demand for the period of 2006–2050 was computed. Different scenarios were generated for external driving factors (population growth, urbanization, and living standards) and the impact of climate change to evaluate their effect on the current water supply system. The results indicated that the future unmet water demand is likely to reach 134 million cubic meters (mcm) by the year 2050 and that the external driving factors are putting more pressure on the supply service. This study further explores the importance of proposed dams (likely to be built until 2025) by WAPDA (Water and Power Development Authority). These dams will decrease the unmet water demand by 60% in the catchment. The water demands under four scenarios (the reference, moderate future-1, moderate future-2, and management scenarios) were compared. The management scenario analysis revealed that 80% of the water demand coverage could be achieved by the year 2023, which could help in developing sustainable water governance for the catchment. Full article