Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01–d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.

The hydrology of Upper Indus basin is not recognized well due to the intricacies in the climate and geography, and the scarcity of data above 5000 m a.s.l where most of the precipitation falls in the form of snow. The main objective of this study is to measure the contributions of different components of runoff in Upper Indus basin. To achieve this goal, the Modified positive degree day model (MPDDM) was used to simulate the runoff and investigate its components in two catchments of Upper Indus basin, Hunza and Gilgit River basins. These two catchments were selected because of their different glacier coverage, contrasting area distribution at high altitudes and significant impact on the Upper Indus River flow. The components of runoff like snow-ice melt and rainfall-base flow were identified by the model. The simulation results show that the MPDDM shows a good agreement between observed and modeled runoff of these two catchments and the effects of snow and ice are mainly reliant on the catchment characteristics and the glaciated area. For Gilgit River basin, the largest contributor to runoff is rain-base flow, whereas large contribution of snow-ice melt observed in Hunza River basin due to its large fraction of glaciated area. This research will not only contribute to the better understanding of the impacts of climate change on the hydrological response in the Upper Indus, but will also provide guidance for the development of hydropower potential and water resources assessment in these catchments.

Water is essential for life, but it also brings serious risks, especially through water-related disasters. Over the past two decades, about 74% of natural disasters have been water-related, affecting millions worldwide. Glaciers are vital sources of freshwater for drinking, farming, industry, and hydropower. However, melting glaciers accelerated by climate change are forming unstable glacial lakes that can burst and cause Glacier Lake Outburst Floods (GLOFs), posing serious threats to downstream communities. This study focuses on five major river catchments in northern Pakistan: Hunza, Gilgit, Shyok, Astore, and Chitral. These areas have extensive glaciation and important hydropower infrastructure, making them especially vulnerable to GLOFs. Using satellite images from 2015 to 2020, we applied the Normalized Difference Water Index (NDWI) a technique that highlights water bodies using satellite data to map and monitor glacial lakes. An automated method helped identify around 1,500 glacial lakes, covering a total area of 62.25 km². Out of these, 36 lakes were classified as potentially hazardous. Most lakes (82%) are small, under 0.05 km², and lie at elevations between 3,400 and 5,100 meters, with the highest concentration (43%) between 4,000 and 4,500 meters. The study did not include en-glacial or sub-glacial lakes, as they require ground-based tools/equipment to detect. This research offers valuable baseline data for understanding glacial lake hazards in Pakistan. It emphasizes the need to include GLOF risk in national and regional planning and supports future efforts in hazard modeling and disaster risk reduction strategies.