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Water resources planning and management depend on the quality of climatic data, particularly rainfall data, for reliable hydrological modeling. This can be very problematic in transboundary rivers with limited disclosing of data among the riparian countries. Satellite precipitation products are recognized as a promising source to substitute the ground-based observations in these conditions. This research aims to assess the feasibility of using a satellite-based precipitation product for better hydrological modeling in an ungauged and riparian river in Pakistan, i.e., the Chenab River. A semidistributed hydrological model of The soil and water assessment tool (SWAT) was set up and two renowned satellite precipitation products, i.e., global precipitation mission (GPM) IMERG-F v6 and tropical rainfall measuring mission (TRMM) 3B42 v7, were selected to assess the runoff pattern in Chenab River. The calibration was done from 2001–2006 with two years of a warmup period. The validation (2007–2010) results exhibit higher correlation between observed and simulated discharges at monthly timescale simulations, IMERG-F (R2 = 0.89, NSE = 0.82), 3B42 (R2 = 0.85, NSE = 0.72), rather than daily timescale simulations, IMERG-F (R2 = 0.66, NSE = 0.61), 3B42 (R2 = 0.64, NSE = 0.54). Moreover, the comparison between IMERG-F and 3B42, shows that IMERG-F is superior to 3B42 by indicating higher R2, NSE and lower percent bias (PBIAS) at both monthly and daily timescale. The results are strengthened by Taylor diagram statistics, which represent a higher correlation (R) and less RMS error between observed and simulated values for IMERG-F. IMERG-F has great potential utility in the Chenab River catchment as it outperformed the 3B42 precipitation in this study. However, its poor skill of capturing peaks at daily timescale remains, leaving a room for IMERG-F to improve its algorithm in the upcoming release.

Pakistan is among the countries that are highly vulnerable to climate change. The country has experienced severe floods and droughts during recent decades. The agricultural sector in Pakistan is adversely affected by climate change. This systematic review paper set out to analyze the existing literature on adaptation measures at the farm level toward climate change in Pakistan. Adopting a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, a total of 62 articles were identified from the Web of Science and Scopus databases. The review paper indicates that the main adaptation strategies adopted by farmers are as follows: changing cropping practices, changing farm management techniques, advanced land use management practices, and nonagriculture livelihood options. Further, this review shows the factors influencing the farmer’s adaptation measures to climate change. Influencing factors were examined and classified into three groups: demographic, socioeconomic, and resources and institutional. Barriers hindering farmers’ adaptive capacity were identified as lack of access to information and knowledge, lack of access to extension services, lack of access to credit facility, and lack of farm resources.

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The global hydrological cycle is susceptible to climate change (CC), particularly in underdeveloped countries like Pakistan that lack appropriate management of precious freshwater resources. The study aims to evaluate CC impact on stream flow in the Soan River Basin (SRB). The study explores two general circulation models (GCMs), which involve Access 1.0 and CNRM-CM5 using three metrological stations (Rawalpindi, Islamabad, and Murree) data under two emission scenarios of representative concentration pathways (RCPs), such as RCP-4.5 and RCP-8.5. The CNRM-CM5 was selected as an appropriate model due to the higher coefficient of determination (R2) value for future the prediction of early century (2021–2045), mid-century (2046–2070), and late century (2071–2095) with baseline period of 1991–2017. After that, the soil and water assessment tool (SWAT) was utilized to simulate the stream flow of watersheds at the SRB for selected time periods. For both calibration and validation periods, the SWAT model’s performance was estimated based on the coefficient of determination(R2), percent bias (PBIAS), and Nash Sutcliffe Efficiency (NSE). The results showed that the average annual precipitation for Rawalpindi, Islamabad, and Murree will be decrease by 43.86 mm, 60.85 mm, and 86.86 mm, respectively, while average annual maximum temperature will be increased by 3.73 °C, 4.12 °C, and 1.33 °C, respectively, and average annual minimum temperature will be increased by 3.59 °C, 3.89 °C, and 2.33 °C, respectively, in early to late century under RCP-4.5 and RCP-8.5. Consequently, the average annual stream flow will be decreased in the future. According to the results, we found that it is possible to assess how CC will affect small water regions in the RCPs using small scale climate projections.