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Winter (December to March) precipitation is the major source of rainfed agriculture, storage, and perennial water flow in the western river system of Pakistan. Hence, this study uses precipitation data and variables of land–ocean and atmosphere from the Pakistan Meteorological Department and European Centre for Medium-Range Weather Forecasts (ECMWF) and fifth-generation reanalysis data (ERA5), respectively, to investigate the changes in winter precipitation and its sensitivity to different land–ocean and atmosphere variables, which are rarely investigated in Pakistan. Non-parametric techniques, such as the modified Mann–Kendal, Sen slope, kernel density-based probability function (PDF), empirical orthogonal function (EOF), and correlation analysis, were used to assess the changes and modes of variability in winter precipitation. The overall seasonal precipitation showed a significant decreasing trend with a (−0.1 mm d−1 yr−1) in the seasonal mean and monthly precipitation, except in February which showed a significant increase (>0.11 mm d−1 yr−1). The highest decrease in daily precipitation (<−0.1 mm d−1 yr−1) was in the north, with a moderate decrease in the southeast. The extreme precipitation indices exhibited an erratic decreasing tendency, but the maximum daily precipitation index increased; post-2000 precipitation extremes displayed an increase, and the seasonal and monthly precipitation exhibited the highest deviations during the drought period (1995–2000). The leading precipitation mode (EOF1) is sensitive to the local land surface processes and significantly correlated (>0.60) with the central Pacific and Indian Ocean's basin-wide sea surface temperature, corroborating the influence of ENSO-induced meridional/zonal deviation of Hadley–Walker circulations. The Hadley and Walker cells affect the south-westerlies' jet stream strength, impacting the water vapor transport and precipitation over Pakistan. These changes in the precipitation magnitude will affect rain-fed agriculture, especially the Rabi cropping pattern and perennial river flow. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the performance of global climate models (GCMs) from the family of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and select the best performing GCMs for future projection of precipitation in Pakistan under multiple shared socioeconomic pathways (SSPs). The spatiotemporal performance of GCMs was evaluated against the Climate Research Unit (CRU) data in simulating annual precipitation during 1951–2014, using the Taylor diagram and interannual variability skill (IVS). Moreover, the modified Mann–Kendall (mMK) and Sen's slope estimator (SSE) tests were employed to estimate significant trends in future precipitation for the period 2015–2100. Based on the comprehensive ranking index (CRI), the HadGEM3‐GC31‐MM model has the highest skill in simulating precipitation distributions followed by EC‐Earth3‐Veg‐LR, CNRM‐ESM2‐1, MPI‐ESM1‐2‐HR, CNRM‐CM6‐1, MRI‐ESM2‐0, CNRM‐CM6‐1‐HR, EC‐Earth3‐Veg, MCM‐UA‐1‐0, INM‐CM5‐0, KACE‐1‐0‐G, CAMS‐CSM1‐0, and HadGEM3‐GC31‐LL models. Furthermore, the projections of the best models ensemble mean (BMEM) showed that the study region will experience a substantial increase in precipitation under SSP3‐7.0 and SSP5‐8.5 but an indolent rise under SSP1‐2.6 and SSP2‐4.5 scenarios. The summer and annual precipitations exhibit a statistically significant increasing trend relative to the winter season under most scenarios. Moreover, the magnitude of monotonic trends in seasonal and annual precipitation progresses from low forcing scenario (SSP1‐2.6) to high forcing scenario (SSP5‐8.5). The findings of the study could provide a benchmark in selecting appropriate GCMs for future projection over a data scare region, like Pakistan. Moreover, the projected trends of future precipitation are crucial in devising adaption and mitigation actions towards sustainable planning of water resource management, food security, and disaster risk management. [ABSTRACT FROM AUTHOR]

The successful exploration and production of shale-gas resources in the United States and Canada sets a new possible solution towards the energy crisis presently affecting most countries of Asia. This study focuses on the use of well log and 2D seismic data for the characterization of the shale oil/gas potential of a Paleocene-Eocene succession - the Meyal area in the Potwar Basin of Pakistan. Two shaly plays are identified in Paleocene-Eocene strata in well logs using Δ Log R and modified Δ Log R cross-plot techniques. The results indicate that Paleocene shale (the Patala Formation) and the lower shaly part of Eocene limestone (Sakesar Formation) can be potentially mature source rocks. However, the thermal maturity modelling proves that only the Paleocene shale is mature. Our results also suggest that the maturity responses on Δ Log R models for the lower shaly part of the Eocene limestone are due to trapped hydrocarbons in the intra-formational fractures. Petroelastic/petrophysical analysis of the Patala Formation reveals two potential shale oil/gas zones on the basis of Young's modulus, Poisson's ratio, Brittleness index and Total Organic Content at an exploitation depth of 3980-3988 m. This work can provide valuable insight for estimating shale oil/gas potential in highly deformed basins not only in Asia but in other parts of the world. [ABSTRACT FROM AUTHOR]

Drought severity still remains a serious concern across Sub-Saharan Africa (SSA) due to its destructive impact on multiple sectors of society. In this study, the interannual variability and trends in the changes of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith (scPDSIPM) and Thornthwaite (scPDSITH) methods for measuring potential evapotranspiration (PET), precipitation (P), normalized difference vegetation index (NDVI), and sea surface temperature (SST) anomalies were investigated through statistical analysis of modeled and remote sensing data. It was shown that scPDSIPM and scPDSITH differed in the representation of drought characteristics over SSA. The regional trend magnitudes of scPDSI in SSA were 0.69 (scPDSIPM) and 0.2 mm/decade (scPDSITH), with a difference in values attributed to the choice of PET measuring method used. The scPDSI and remotely sensed-based anomalies of P and NDVI showed wetting and drying trends over the period 1980–2012 with coefficients of trend magnitudes of 0.12 mm/decade (0.002 mm/decade). The trend analysis showed increased drought events in the semi-arid and arid regions of SSA over the same period. A correlation analysis revealed a strong relationship between the choice of PET measuring method and both P and NDVI anomalies for monsoon and pre-monsoon seasons. The correlation analysis of the choice of PET measuring method with SST anomalies indicated significant positive and negative relationships. This study has demonstrated the applicability of multiple data sources for drought assessment and provides useful information for regional drought predictability and mitigation strategies. [ABSTRACT FROM AUTHOR]

In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency's essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration's (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China's Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B's contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product. [ABSTRACT FROM AUTHOR]

Assessing the long-term precipitation changes is of utmost importance for understanding the impact of climate change. This study investigated the variability of extreme precipitation events over Pakistan on the basis of daily precipitation data from 51 weather stations from 1980-2016. The non-parametric Mann–Kendall, Sen's slope estimator, least squares method, and two-tailed simple t-test methods were used to assess the trend in eight precipitation extreme indices. These indices were wet days (R1 ≥1 mm), heavy precipitation days (R10 ≥ 10 mm), very heavy precipitation days (R20 ≥ 20 mm), severe precipitation (R50 ≥ 50 mm), very wet days (R95p) defining daily precipitation ≥ 95 percentile, extremely wet days (R99p) defining daily precipitation ≥ 99 percentile, annual total precipitation in wet days (PRCPTOT), and mean precipitation amount on wet days as simple daily intensity index (SDII). The study is unique in terms of using high stations' density, extended temporal coverage, advanced statistical techniques, and additional extreme indices. Furthermore, this study is the first of its kind to detect abrupt changes in the temporal trend of precipitation extremes over Pakistan. The results showed that the spatial distribution of trends in different precipitation extreme indices over the study region increased as a whole; however, the monsoon and westerlies humid regions experienced a decreasing trend of extreme precipitation indices during the study period. The results of the sequential Mann–Kendall (SqMK) test showed that all precipitation extremes exhibited abrupt dynamic changes in temporal trend during the study period; however, the most frequent mutation points with increasing tendency were observed during 2011 and onward. The results further illustrated that the linear trend of all extreme indices showed an increasing tendency from 1980- 2016. Similarly, for elevation, most of the precipitation extremes showed an inverse relationship, suggesting a decrease of precipitation along the latitudinal extent of the country. The spatiotemporal variations in precipitation extremes give a possible indication of the ongoing phenomena of climate change and variability that modified the precipitation regime of Pakistan. On the basis of the current findings, the study recommends that future studies focus on underlying physical and natural drivers of precipitation variability over the study region. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article describes a study which demonstrated the applications of Ground Penetrating Radar (GPR) in geology and civil engineering in Pakistan. GPR was deployed in the Motorway M1 Islamabad-Peshawar, roof floor of the buildings in Islamabad, and investigation of rupture in Balakot developed due to the 2005 Kashmir earthquake. Reviews of the operation of GPR systems are given as well as a discussion on data processing and data interpretation techniques.

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 (T min), maximum (T max), and mean temperature (T mean) 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 T min , T max , and T mean 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 T min , T max , and T mean over SA. In terms of projected changes, T min , T max , and T mean 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 T min , T max , and T mean 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 T min , T max , and T mean under all warming targets. The uneven and intensified patterns of T min , T max , and T mean 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. • The performance of the 21 CMIP5 models is evaluated over South Asia. • The selected models are ranked according to their spatiotemporal skills. • The future changes in temperature are estimated over South Asia relative to the pre-industrial levels. • Three different global warming targets are used i.e., 1.5 °C, 2 °C, and 3 °C. [ABSTRACT FROM AUTHOR]