Pakistan’s agriculture and food production account for 27% of its overall gross domestic product (GDP). Despite ongoing advances in technology and crop varieties, an imbalance between water availability and demand, combined with robust shifts in drought propagation has negatively affected the agro-ecosystem and environmental conditions. In this study, we examined hydro-meteorological drought propagation and its associated impacts on crop yield across natural and human-disturbed agro-ecological zones (AEZs) in Pakistan. Multisource datasets (i.e., ground observations, reanalysis, and satellites) were used to characterize the most extensive, intense drought episodes from 1981 to 2018 based on the standardized precipitation evaporation index (SPEI), standardized streamflow index (SSFI), standardized surface water storage index (SSWSI), and standardized groundwater storage index (SGWI). The most common and intense drought episodes characterized by SPEI, SSFI, SSWSI, and SGWI were observed in years 1981–1983, 2000–2003, 2005, and 2018. SPEI yielded the maximum number of drought months (90) followed by SSFI (85), SSWSI (75), and SGWI (35). Droughts were frequently longer and had a slower termination rate in the human-disturbed AEZs (e.g., North Irrigated Plain and South Irrigated Plain) compared to natural zones (e.g., Wet Mountains and Northern Dry Mountains). The historical droughts are likely caused by the anomalous large-scale patterns of geopotential height, near-surface air temperature, total precipitation, and prevailing soil moisture conditions. The negative values (<−2) of standardized drought severity index (DSI) observed during the drought episodes (1988, 2000, and 2002) indicated a decline in vegetation growth and yield of major crops such as sugarcane, maize, wheat, cotton, and rice. A large number of low-yield years (SYRI ≤ −1.5) were recorded for sugarcane and maize (10 years), followed by rice (9 years), wheat (8 years), and cotton (6 years). Maximum crop yield reductions relative to the historic mean (1981–2017) were recorded in 1983 (38% for cotton), 1985 (51% for maize), 1999 (15% for wheat), 2000 (29% for cotton), 2001 (37% for rice), 2002 (21% for rice), and 2004 (32% for maize). The percentage yield losses associated with shifts in SSFI and SSWSI were greater than those in SPEI, likely due to longer drought termination duration and a slower termination rate in the human-disturbed AEZs. The study’s findings will assist policymakers to adopt sustainable agricultural and water management practices, and make climate change adaptation plans to mitigate drought impacts in the study region.

Intensive groundwater abstraction has augmented socio-economic development worldwide but threatens the sustainability of groundwater resources. Spatiotemporal analysis of groundwater storage changes is a prerequisite to sustainable water resource management over river basins. To estimate the groundwater storage changes/anomalies (GWCs) in the Indus River Basin (IRB), where observation wells are sparse, Gravity Recovery and Climate Experiment, the Global Land Data Assimilation System, and the WaterGAP Hydrological Model data were employed. The groundwater storage changes and controlling factors were investigated at three tier levels (TTLs), i.e., the basin, river reach, and region, to explore their implications on regional water resource management and provide management options at each level. Overall, the IRB groundwater declined from January 2003 to December 2016, with a relatively higher rate during 2003–2009 than during 2010–2016. Spatially, according to a reach-specific analysis, 24%, 14%, and 2% of the upper, middle, and lower reaches of the IRB, respectively, were indicated by a ‘severe groundwater decline’ over the entire period (i.e., 2003–2016). The GRACE-based GWCs were validated with in situ data of two heterogeneous regions, i.e., Kabul River Basin (KRB) and Lower Bari Doab Canal (LBDC). The analysis showed a correlation (R²) of 0.77 for LBDC and 0.29 for KRB. This study’s results reveal that climatic variations (increase in evapotranspiration); anthropogenic activities, i.e., pumping for irrigation; and water allocations in these regions mainly drive the groundwater storage changes across the Indus Basin.

Environmental flows (EFs), essential for upholding the ecological integrity of rivers and aquatic habitats, have been disrupted significantly by diverting water for agricultural, industrial and domestic uses. This underscores the imperative of implementing sustainable water resource management to harmonize agricultural and environmental needs. The study was conducted in the Indus River Basin (IRB), a region extensively transformed by human interventions. EFs were determined through various techniques, including the flow duration curve shifting method, flow duration curve analysis, low‐flow indices, the Tennant method, the Smakhtin approach, the Tessmann method and the Pastor method. Analysing the estimated EFs alongside downstream flows unveiled specific timeframes (days, months and seasons) of unmet EF requirements. To safeguard the downstream ecosystems, the following EFs were estimated for the respective locations: 880 m³/s (38% of the mean annual flow [MAF]) for the Indus River at Tarbela Dam, 412 m³/s (48% of the MAF) for the Jhelum at Mangla Dam, 425 m³/s (44% of the MAF) for the Chenab at Marala headworks, 389 m³/s (56% of MAF) for the Ravi at Balloki headworks, 184 m³/s (50% of MAF) for the Sutlej at Sulemanki headworks and 231 m³/s (38% of MAF) below Kotri Barrage. The study revealed that violations of EFs occurred 41%, 43%, 44% and 52% of the time during the study period for the Chenab at Marala headworks, the Ravi at Balloki headworks, the Sutlej at Sulemanki headworks and the Indus River at the Kotri Barrage, respectively. The results highlighted that the Chenab, Ravi and Sutlej rivers are particularly susceptible to vulnerability, as the estimated EFs were not consistently upheld in these rivers. These findings underscore the urgent need to take appropriate measures to ensure EFs are not violated, thus safeguarding the downstream ecosystems.

The SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) named COVID-19 (coronavirus 2019) pandemic affected humanity globally and highly influenced the healthcare community as well. Several crises, including health threats, came forth internationally due to COVID-19 infection and transmission worldwide. The reported clinical symptoms include sore throat, breathing difficulty, cough, fever, and tiredness. In comparison, most infections are lung-related and gastrointestinal in SARS-CoV-2 patients. It is observed that lung infections possibly occur due to epidemiologic or bacteriological infections, physical injury, or hazardous particles inhalation. Advanced machine learning techniques play a vital role in the diagnosis of COVID-19 infection from lungs’ X-ray images. Accordingly, this chapter presents deep learning techniques for big data of COVID-19 infection cases’ diagnostics, prevention, and treatment on public datasets. Finally, analysis and evidence-based findings of existing techniques on benchmark datasets and modalities are presented, leading to the conclusion discussed in depth.