Agriculture of Pakistan relies on the Indus basin, which is facing severe water scarcity conditions. Poor irrigation practices and lack of policy reforms are major threats for water and food security of the country. In this research, alternative water-saving strategies are evaluated through a high spatio-temporal water footprint (WF) assessment (1997–2016) for the Punjab and Sindh provinces, which cover an irrigated area of 17 million hectares in the Indus basin of Pakistan. The SPARE:WATER model is used as a spatial decision support tool to calculate the WF and establish alternative management plans for more sustainable water use. The average water consumption (WF_area) is estimated to 182 km³ yr⁻¹, composed of 75% blue water (irrigation water from surface water and groundwater sources), 17% green water (precipitation) and 8% grey water (water used to remove soil salinity or dilute saline irrigation water). Sugarcane, cotton, and rice are highly water-intensive crops, which consume 57% of the annual water use. However, WF_area can be reduced by up to 35% through optimized cropping patterns of the existing crops with the current irrigation settings and even by up to 50% through the combined implementation of optimal cropping patterns and improved irrigation technologies, i.e., sprinkler and drip irrigation. We recommend that the economic impact of these water-saving strategies should be investigated in future studies to inform stakeholders and policymakers to achieve a more sustainable water policy for Pakistan.

Floods are brutal, catastrophic natural hazards which affect most human beings in terms of economy and life loss, especially in the large river basins worldwide. The Indus River basin is considered as one of the world’s large river basins, comprising several major tributaries, and has experienced severe floods in its history. There is currently no proper early flood warning system for the Indus River which can help administrative authorities cope with such natural hazards. Hence, it is necessary to develop an early flood warning system by integrating a hydrodynamic model, in situ information, and satellite imagery. This study used Hydrologic Engineering Center–River Analysis System (HEC–RAS) to predict river dynamics under extreme flow events and inundation modeling. The calibration and validation of the HEC–RAS v5 model was performed for 2010 and 2015 flood events, respectively. Manning’s roughness coefficient (n) values were extracted using the land use information of the rivers and floodplains. Multiple combinations of n values were used and optimized in the simulation process for the rivers and floodplains. The Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09A1, and MOD09GA products were used in the analysis. The Normalized Difference Water Index (NDWI), Modified NDWI1 (MNDWI1), and MNDWI2, were applied for the delineation of water bodies, and the output of all indices were blended to produce standard flood maps for accurate assessment of the HEC–RAS-based simulated flood extent. The optimized n values for rivers and floodplains were 0.055 and 0.06, respectively, with significant satisfaction of statistical parameters, indicating good agreement between simulated and observed flood extents. The HEC–RAS v5 model integrated with satellite imagery can be further used for early flood warnings in the central part of the Indus River basin.

Over the years, the household sector has become an important energy consumer and the main source of greenhouse gas (GHG) emissions. The rural household sector has significant potential for emission reduction due to its heavy reliance on traditional fuels and technologies. A great number of academic studies have been undertaken to analyze patterns of household energy and their determinants around the globe, particularly in developing countries. However, little is known about the association between household dynamics and patterns of energy (biomass vs. non-renewable) use. This study aims to analyze the relationship between different household dynamics, such as household size, income, climate, availability of resources, markets, awareness, consumption of energy, and carbon emissions. The study uses the STIRPAT model to investigate the impact of income, household size, housing dimensions, clean energy, and market accessibility on energy consumption. The findings of the study reveal that biomass energy accounts for the majority of household energy consumption and dung has the highest share in total household energy consumption (39.11%) The consumption of biomass increased with the size of the household and decreased with the level of income. A 1 kgoe increase in biomass consumption resulted in a 15.355 kg increase in CO₂ emissions; on the other hand, a 1 kgoe increase in non-renewable-energy consumption resulted in just a 0.8675 kg increase in CO₂ emissions. The coefficients of housing unit size, distance from the LPG market, and livestock were the primary determinants for choosing any fuel. Having knowledge of modern cookstoves, clean energy, and the environmental impact of fuels reduced the consumption of both energy sources. Furthermore, it was found that households with a greater reliance on biomass emitted higher quantities of carbon compared to those with a low reliance on biomass. Based on the results of the study, it can be stated that a reduction in the use of biomass and non-renewable energy is possible with adequate interventions and knowledge.

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.