This study focused on the trends of rainfall variability and drought monitoring in the northern region of Pakistan (Gilgit-Baltistan). Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) model data were used for the period of 1981 to 2020. The Standardized Precipitation Index (SPI) was applied to assess the dry and wet conditions during the study period. The Mann–Kendall (MK) and Spearman’s rho (SR) trend tests were applied to calculate the trend of drought. A coupled model intercomparison project–global circulation model (CMIP5–GCMs) was used to project the future precipitation in Gilgit-Baltistan (GB) for the 21st century using a multimodel ensemble (MME) technique for representative concentration pathway (RCP) 4.5 and RCP 8.5. From the results, the extreme drought situations were observed in the 12-month SPI series in 1982 in the Diamir, Ghizer, and Gilgit districts, while severe drought in 1982–1983 was observed in Astore, Ghizer, Gilgit, Hunza-Nagar, and Skardu. Similarly, in 2000–2001 severe drought prevailed in Diamir, Ghanche, and Skardu. The results of MK and SR indicate a significant increasing trend of rainfall in the study area, which is showing the conversion of snowfall to rainfall due to climate warming. The future precipitation projections depicted an increase of 4% for the 21st century as compared with the baseline period in the GB region. The results of the midcentury projections depicted an increase in precipitation of about 13%, while future projections for the latter half of the century recorded a decrease in precipitation (about 9%) for both RCPs, which can cause flooding in midcentury and drought in the latter half of the century. The study area is the host of the major glaciers in Pakistan from where the Indus River receives its major tributaries. The area and volume of these glaciers are decreasing due to warming impacts of climate change. Therefore, this study is useful for proper water resource management to cope up with water scarcity in the future.

Climate change is one of the leading issues affecting river basins due to its direct impacts on the cryosphere and hydrosphere. General circulation models (GCMs) are widely applied tools to assess climate change but the coarse spatial resolution of GCMs limit their direct application for local studies. This study selected five CMIP5 GCMs (CCSM4, HadCM3, GFDL-CM3, MRI-CGCM3 and CanESM2) for performance evaluation ranked by Nash–Sutcliffe coefficient (NSE) and Kling–Gupta Efficiency (KGE). CCSM4 and HadCM3 large-scale predictors were favored based on ranks (0.71 and 0.68, respectively) for statistical downscaling techniques to downscale the climatic indicators T_max, T_min and precipitation. The performance of two downscaling techniques, Statistical Downscaling Methods (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG), were examined using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), bias, NSE and KGE with weights (W_i) for the validation period. The results of statistical measures proved SDSM more efficient (0.67) in comparison to the LARS-WG (0.51) for the validation time for the Jhelum River basin. The findings revealed that the SDSM simulation for T_max and T_min are more comparable to the reference data for the validation period except simulation of extreme events by precipitation. The 21st century climatic projections exhibited a significant rise in T_max (2.37–4.66 °C), T_min (2.47–4.52 °C) and precipitation (7.4–11.54%) for RCP-4.5 and RCP-8.5, respectively. Overall, the results depicted that winter and pre-monsoon seasons were potentially most affected in terms of warming and precipitation, which has the potential to alter the cryosphere and runoff of the Jhelum River basin.

About 41% of the earth is drought-affected, which has impacted nearly 2 billion people, and it is expected that more than 90% of terrestrial areas will be degraded by 2050. To evade and mitigate the harmful impacts of drought, it is necessary to study the rainfall variability and assess the drought trend at a global and regional level. This study utilized 70 meteorological stations in South Korea to evaluate the rainfall variability, drought, and its trend during the past five decades using the standardized precipitation evapotranspiration index (SPEI) and the standardized precipitation index (SPI). Rainfall data normality was assessed with mean, standard deviation, skewness, and kurtosis. The highest amount of rainfall was observed in the months of June, July, and August. The SPI and SPEI 12-month results revealed that 1982, 1988, 2008, 2015, and 2017 were dry years throughout the country, while from 2013 to 2017 mixed drought events were observed for the 6-month time series. The Mann-Kendall trend test was applied to the 1- and 12-month time series, and the results revealed that the months of January, March, April, May, June, and August had a significant negative trend, which means drought is increasing in these months, while the months of September, October, and December had a significant positive trend, which means wetter conditions prevailed in these months during the study period. It was observed in the 12-month time series that only two met stations had a significant negative trend, while only one had a significant positive trend. It was found that January and March were the driest months, and October was the wettest month. The detected drought events in this research are consistent with ENSO events. We have observed differences in drought characteristics (duration and frequency) for both indices. Climatic data revealed that South Korea has faced drought conditions (rainfall deficit) due to a shortened monsoon season. This study can provide guidance on water management strategies under the changing pattern of drought in South Korea.

Climate change is a global issue and causes great uncertainties in runoff and streamflow projections, especially in high-altitude basins. The quantification of climatic indicators remains a tedious job for the scarcely gauged mountainous basin. This study investigated climate change by incorporating GCM (CCSM4) using the SDSM method for RCPs in the Jhelum river basin. Historical climatic data were coupled with Aphrodite data to cope with the scarcity of weather stations. SDSM was calibrated for the period 1976–2005 and validated for the period 2006–2015 using R2 and RMSE. Future climatic indicators were downscaled and debiased using the MB-BC method. The de-biased downscaled data and MODIS data were used to simulate discharge of Jhelum river basin using SRM. Simulated discharge was compared with measured discharge by using Dv% and NSE. The R2 and RMSE for SDSM range between 0.89–0.95 and 0.8–1.02 for temperature and 0.86–0.96 and 0.57–1.02 for precipitation. Projections depicted a rising trend of 1.5 °C to 3.8 °C in temperature, 2–7% in mean annual precipitation and 3.3–7.4% in discharge for 2100 as compared to the baseline period. Results depicted an increasing trend for climatic indicators and discharge due to climate change for the basin.

The current study focuses on tracking urban sprawl in one of the rapidly growing cities of Pakistan i.e., Sargodha. The secondary cities have the capacity to persuade people from rural areas to relocate. In this regard, the current study is unique in that it will give a comprehensive analysis of urban sprawl of Sargodha City. The remotely sensed data is used for this purpose. The study is primarily based on the collecting of both primary and secondary data. For the last 30 years, from 1987 to 2017, primary data was gathered from the United States Geological Survey (USGS) and the Global Land Cover Facility (GLCF). ERDAS Imagine 2013 is used to classify land use using remotely sensed data. The Kappa Coefficient was used to compute the accuracy assessment of classified maps. Maps are used to depict a comparative analysis of urban sprawl in the city. In addition, regression analysis and simple statistical computations are also utilised to assess dynamic changes in urban sprawl. From 1987 to 2017, a 10-year interval was used to measure change in built-up land using an equation. According to the findings of the study, Sargodha City has experienced considerable changes in land use patterns. This current study is beneficial for policymakers to design the city in a well-planned manner. The appropriate design of cities can pave the way for higher living standards in rapidly growing cities of Pakistan.