• Energy Research

Due to climate change, the Northwesterner Gilgit Baltistan's, Ghizer district is highly susceptible to glacial lake outburst floods (GLOFs). Nearly 24 GLOFs have occurred in this area in the last ∼200 years, demonstrating the growing recurrent nature of these incidents. Taking this into account, the assessment of risks associated with GLOFs was investigated in this study. All regional glacial lakes were identified in the first phase, and changes between 2000 and 2023 were mapped using moderate-resolution satellite images (Landsat). To map built-up and agricultural areas, Landsat's lower resolution limited its use in such complex topography. Therefore, Sentinel-2 data were used, and images from 2016 to 2023 were classified using a random forest classifier. A total of 617 glacial lakes covering ∼31.67 km2 of the area were mapped in 2023. Since 2000, ∼88 glacial lakes have appeared, showing an increasing trend in the number of lakes. In the second phase, categorization and susceptibility to GLOFs were assessed using multicriteria decision analysis. The grass GIS tool, r.avaflow, was used to generate GLOFs simulations based on friction, density, release area, travel time, and two travel time scenarios, i.e., 1800 and 3600 s, for four high-weighted glacial lakes. Results showed that the glacial lake near Darkut village, Yaseen Valley, poses a significant threat to downstream communities. In contrast, two other lakes in Gupis Valley will have a moderate effect on the infrastructure and agriculture. The glacial lake of Punyal Valley poses no significant threat.

The aim of present work was to evaluate the effects of titanium dioxide nanoparticles (TiO2 NPs) on rice's growth (Oryza sativa L.) and nutrient availability under different soil textures. Greenhouse experiment was carried out with three soil textures (sandy loam, silt loam and silty clay loam) and two concentrations of TiO2 NPs (500, 750 mg kg-1). Control (without TiO2 NPs) was also maintained for the comparison. Growth parameters including chlorophyll content, root/shoot length, fresh/dry biomass and nutrients' uptake including calcium (Ca), copper (Cu), iron (Fe), magnesium (Mg), phosphorous (P), potassium (K) and zinc (Zn) were determined. The results revealed that application of 500 mg kg-1 TiO2 NPs in silty clay loam soil increased the chlorophyll content (3.3-folds), root length (49%), shoot length (31%), root and shoot biomass (41% & 39%, respectively) as compared to other soil textures. The maximum plant growth was observed in silty clay loam > silt loam > sandy loam. Concentration of Cu, Fe, P and Zn in shoot was increased by 8 - , 2.3 - , 0.4 - , 0.05 -folds in silty clay loam upon 500 mg kg-1 TiO2 NPs application as compared to the control. Backward selection method to model the parameters (nutrients in soil) for the response variables (root/shoot length and biomass) showed that Ca, Fe, P are the main nutrients responsible for the increase in plant length and biomass. Overall, the growth of rice was better in silty clay loam at 500 mg kg-1 of TiO2 NPs.

Biased distribution of hydro-climate stations in high elevations are major obstacles for reliable appraisal of the hydro-climatic regime of the Chitral Basin located in the extreme north of Pakistan. We modeled this regime in the ARC-SWAT hydrological model forced with the latest gridded reanalysis ERA5 Land dataset, bias-corrected against a good quality reference dataset. The performance of the gridded dataset was cross-validated by comparing the model flow simulation against the observed flows. The ERA5 Land overall provided reasonably good estimates. The calibrated model on the daily time scale was able to provide excellent values of the employed statistical measures (NSE, KGE, PBIAS, RMSE and MAE). For a future climate change analysis, climate series was devised using two future projection scenarios (RCP4.5 and RCP8.5) using the best performing GCM (MIROC5_rlilp1) out of five investigated GCMs. The results of the climate change analysis reveal increment in the average temperature up to +3.73 °C and +5.62 °C for RCP4.5 and RCP8.5, respectively, while the analysis of precipitation suggests an annual decrease up to −16% and −35% against RCP4.5 and RCP8.5, respectively, by the end of century. A future simulated flow analysis showed an increment of +0.25 % and decrease of −6.82% for RCP4.5 and RCP8.5, respectively. Further analysis of climate suggests seasonal deflections especially in precipitation and flow regimes. A notable climb in flow quantities was observed during spring season (MAM) in spite of the major reduction in precipitation amounts for that season. This implicitly supports a high rate of glacial/snow melt especially in the spring season during that period. Frequent droughts and floods are also projected by examining flow durations at each interval of the 21st century.

Abstract Land use land cover (LULC) change determination caused by development projects is always mandatory as land is the major source of local livelihoods and regional economy. Worldwide, very limited studies have been conducted to determine LULC changes caused by run-of-the-river projects, which are generally considered safe due to their design. Present study used Google Earth Engine (GEE) to examine the LULC changes caused by Ghazi Barotha Hydropower Project (GBHP), which is a run-of-the-river project, built in 2002 on Indus River in Pakistan. The project diverts river water from Ghazi barrage, for a 6,600 GWh annual power production, through an open concrete power channel of 100 m width and 9 m depth. Field surveys were carried out to assess respondents’ opinions about LULC changes and their major causes. LULC determination was carried out from 1990–2020 through processing Landsat images in GEE, and Random Forest (RF) machine learning technique was used for supervised classification of the study area. 384 respondents were consulted during the field survey and their responses were collected using semi-structured self-administered proformas. Results showed that after functioning, GBHP caused major LULC changes in project downstream areas from 2002–2010, as there was a significant decrease in area under agriculture by 29.10% and 47%, during summer and winter seasons respectively. The trend was concurrent with a decrease in area under water and was also followed by a marked increase in area under vegetation and baresoil. However, from 2010–2020, agriculture area again increased by 75.61% and 84.53% in summer and winter seasons respectively, as compared to 2002–2010. Respondents during the field survey revealed that agriculture reduction from 2002–2010 was due to water scarcity caused by GBHP; also leading to vegetation and baresoil increase. Recovery of agriculture from 2010–2020 was attributed to groundwater development intervention, better seeds, and farmers’ capacity building.

Inter alia, inter-annual and spatial variability of climate, particularly rainfall, shall trigger frequent floods and droughts in Pakistan. Subsequently, a higher proportion of the country’s population will be exposed to water-related challenges. This study analyzes and projects the long-term spatio-temporal changes in precipitation using the data from 2005 to 2099 across two large river basins of Pakistan. The plausible precipitation data to detect the projected trends seems inevitable to study the future water resources in the region. For, policy decisions taken in the wake of such studies can be instrumental in mitigating climate change impacts and shape water management strategies. Outputs of the Coupled Model Intercomparison Project 5 (CMIP5) climate models for the two forcing scenarios of RCP 4.5 and RCP 8.5 have been used for the synthesis of projected precipitation data. The projected precipitation data have been synthesized in three steps (1) dividing the area in different climate zones based on the similar precipitation statistics (2) selection of climate models in each climate zone in a way to shrink the ensemble to a few representative members, conserving the model spread and accounting for model similarity in a baseline period of 1971–2004 and the projected period of 2005–2099 and (3) combining the selected model’s data in mean and median combinations. The future precipitation trends were detected and quantified, for the set of four scenarios. The spatial distribution of the precipitation trends was mapped for better understanding. All the scenarios produced consistent increasing or decreasing trends. Significant declining trends were projected in the warm wet season at 0.05% significance level and the increasing trends were projected in cold dry, cold wet and warm dry seasons. Framework developed to project climate change trends during the study can be replicated for any other area. The study therefore can be of interest for researchers working on climate impact modeling.