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The sharp rise in temperature has increased the frequency, intensity, duration, and timing of heat waves (HWs) over different regions of the world. Due to climate change, the China–Pakistan Economic Corridor (CPEC) is one of the highly vulnerable regions to HWs and needs comprehensive research studies to investigate the HW phenomenon in the region. This study analyzed the spatial and temporal changes in the daytime and nighttime HW characteristics based on multiple indices over the CPEC region. We used daily maximum and minimum temperatures (hereafter Tmax and Tmin) of 48 meteorological stations for the time period of 1980–2016. The non-parametric modified Mann–Kendall, Theil–Sen’s test, least square method, student t-test, and chi-square goodness-of-fit test techniques were used to analyze the long-term spatiotemporal changes in the daytime and nighttime HW characteristics. The results of the study show that the number of annual daytime/nighttime HW events, annual sum of participating daytime/nighttime HW days, the average length of annual daytime/nighttime HW events, duration of the longest annual daytime/nighttime HW event, the average magnitude of all annual daytime/nighttime HW events, amplitude of the hottest annual daytime/nighttime HW event, and the ending date of annual last daytime/nighttime HW event exhibited significant increasing trends at the rate of 0.78/1.43 events decade−1, 10/11.82, 2/1.74, 2.16/1.52 days decade−1, 0.40/0.59, 0.24/0.73 °C decade−1, and 12.29/10 days decade−1, respectively. Despite all, the onset date of the annual first daytime/nighttime HW event has shown a significant decreasing trend of − 5.71/− 5 days decade−1. The obvious positive trend of HW behaviors indicates that the country has experienced more frequent, stronger, more intense, and longer HWs during the study period. The spatial pattern of the trend indicates that the southern, central and eastern parts of Pakistan exhibited prominent and consistent HW activities, while the northwestern mountainous regions showed high spatial variability with some stations exhibited decreasing trends in HW indices. The findings of this study will be a base for the projection and mitigation of HWs in the region. Based on the study findings, we recommend that the mechanism of HW and its natural and anthropogenic drivers should be thoroughly investigated over the study region.

Seismic reservoir characterization is a renowned technique to obtain a better understandingand quantification of hydrocarbon bearing reservoirs by integration of seismic attributes, post stack inversion and wireline log analysis. In current case study, a methodology based on these techniques is proposed, and its applicability is tested for delineating the secondary reservoir potential of the Eocene carbonates (Habib Rahi and Sui Upper Limestone) in Qadirpur area, Lower Indus Basin, Pakistan. The application of post stack inversion provides a reasonable estimate of the acoustic impedance and enables the spatial distribution of important petrophysical properties (effective porosity and water saturation) available at sparse well locations. This distribution is facilitated by developing geostatistical relations between seismic and well derived reservoir properties in the range of seismic resolution. The results from all integrated techniques depict fluid saturated zones at Habib Rahi (gas at 907–922 m; water at 953–973 m) and Sui Upper Limestone (gas at 1192–1208 m) levels. The spatial distribution of the reservoir properties (effective porosity and water saturation) obtained via seismic data shows a very good calibration at the location of wells and reflects significant secondary reservoir potential for Eocene carbonates in the study area (effective porosity in the range of 7–26% and water saturation in the range of 35–60%). Furthermore, the methodology tested over here will be advantageous for the characterization of oil/gas reservoirs in various basins of Asia and other parts of world with similar geological settings.

The exploration and production of unconventional resources has increased significantly over the past few years around the globe to fulfill growing energy demands. Hydrocarbon potential of these unconventional petroleum systems depends on the presence of significant organic matter; their thermal maturity and the quality of present hydrocarbons i.e. gas or oil shale. In this work, we present a workflow for estimating Total Organic Content (TOC) from seismic reflection data. To achieve the objective of this study, we have chosen a classic potential candidate for exploration of unconventional reserves, the shale of the Sembar Formation, Lower Indus Basin, Pakistan. Our method includes the estimation of TOC from the well data using the Passey’s ΔlogR and Schwarzkofp’s methods. From seismic data, maps of Relative Acoustic Impedance (RAI) are extracted at maximum and minimum TOC zones within the Sembar Formation. A geostatistical trend with good correlation coefficient (R2) for cross-plots between TOC and RAI at well locations is used for estimation of seismic based TOC at the reservoir scale. Our results suggest a good calibration of TOC values from seismic at well locations. The estimated TOC values range from 1 to 4% showing that the shale of the Sembar Formation lies in the range of good to excellent unconventional oil/gas play within the context of TOC. This methodology of source rock evaluation provides a spatial distribution of TOC at the reservoir scale as compared to the conventional distribution generated from samples collected over sparse wells. The approach presented in this work has wider applications for source rock evaluation in other similar petroliferous basins worldwide.

Deviations in South Asian Summer Monsoon (SASM) precipitation affect the regional floods and drought patterns. In the current study, in-situ observations from Pakistan Meteorological Department (PMD), remotely sensed precipitation data from Climate Hazard Infrared Precipitation with Station data (CHIRPS), reanalysis data from ERA5, and National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) during 1981–2018 are used to explore the atmospheric circulation patterns during above and below-normal precipitation episodes. In a statistical sense, two methods, namely the Empirical Orthogonal Function (EOF) and Percent Normal (PN) indices are used to derive dominant spatial patterns and temporal evolution of extreme monsoon precipitation episodes. Results inferred 60% of the variance to the leading EOF mode depicting a similar spatial pattern of eigenvectors across the region. The leading principal component (PC) and PN index together depicted similar deviations, complementing the extreme flooding and drought years. From the composites, an anomalous increase (decrease) in seasonal precipitation magnitude was observed. The possible mechanism suggests an active control of atmospheric and sea-surface temperature (SST) forcing by altering the wind ascent (descent). The jet streams (200 hPa) intensification of Rossby waves high (low) pressure provides favorable frontal boundaries between polar cold and tropical warm air masses. The westerlies and easterlies are intensified (suppressed) during the above (below) normal precipitation composites, affecting the moisture transport. The enhanced (reduced) convective activities in the Indian Ocean as a primary source affected precipitation in the region during each composite.

This study was designed to evaluate the spatiotemporal performance of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models in the historical simulation and future projections of minimum (Tmin), maximum (Tmax), and mean temperature (Tmean) over South Asia (SA) during global warming of 1.5 oC, 2 oC, and 3 oC targets under RCP4.5 and RCP8.5 scenarios. It is worth mentioning that the present study is the first of its kind to use such a large number of CMIP5 models to project future changes in Tmin, Tmax, and Tmean over SA using three different warming thresholds. The results show that CSIRO–MK3–6–0, MIROC–ESM–CHEM, CNRM–CM5, CCSM4, and MRI–CGCM3 models relatively performed better with a consistent and accurate spatiotemporal simulation of Tmin, Tmax, and Tmean over SA. In terms of projected changes, Tmin, Tmax, and Tmean show a dominating and consistent warming pattern over SA with stronger intensity in higher latitude than mid–low latitudes under 1.5 oC, 2 oC, and 3 oC warming thresholds. The northwestern (eastern) regions of SA will witness greater (least) warming in Tmin, Tmax, and Tmean under all warming thresholds in RCP4.5 and RCP8.5 scenarios. Furthermore, the central and southern parts of SA will experience a moderate increase in Tmin, Tmax, and Tmean under all warming targets. The uneven and intensified patterns of Tmin, Tmax, and Tmean may result in temperature extremes, which would pose potential risks to the local population. Therefore, more attention should be paid on the regional and local perspectives to estimate the adverse impacts of these extremes under different global warming targets. We further suggest to project future changes in climate extremes over SA under different warming levels, which will be helpful in climate change adaptation and mitigation over the study region.

Various state-of-the-art gridded satellite precipitation products (GPPs) have been derived from remote sensing and reanalysis data and are widely used in hydrological studies. An assessment of these GPPs against in-situ observations is necessary to determine their respective strengths and uncertainties. GPPs developed from satellite observations as a primary source were compared to in-situ observations, namely the Climate Hazard group Infrared Precipitation with Stations (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA). These products were compared to in-situ data from 51 stations, spanning 1998–2016, across Pakistan on daily, monthly, annual and interannual time scales. Spatiotemporal climatology was well captured by all products, with more precipitation in the north eastern parts during the monsoon months and vice-versa. Daily precipitation with amount larger than 10 mm showed significant (95%, Kolmogorov-Smirnov test) agreement with the in-situ data, especially TMPA, followed by CHIRPS and MSWEP. At monthly scales, there were significant correlations (R) between the GPPs and in-situ records, suggesting similar dynamics; however, statistical metrics suggested that the performance of these products varies from north towards south. Temporal agreement on an interannual scale was higher in the central and southern parts which followed precipitation seasonality. TMPA performed the best, followed in order by CHIRPS, MSWEP and PERSIANN-CDR.