The world has faced many disasters in recent years, but flood impacts have gained immense importance and attention due to their adverse effects. More than half of global flood destruction and damages occur in the Asia region, which causes losses of life, damage infrastructure, and creates panic conditions among the communities. To provide a better understanding of flood hazard management, flood vulnerability assessment is the primary objective. In this case, vulnerability is the central construct in flood analysis and assessment. Many researchers have defined different approaches and methods to understand vulnerability assessment and how geographic information systems assess the flood vulnerability and their associated risk. Geographic information systems track and predict the disaster trend and mitigate the risk and damages. This study systematically reviews the methodologies used to measure floods and their vulnerabilities by integrating geographic information system. Articles on flood vulnerability from 2010 to 2020 were selected and reviewed. Through the systematic review methodology of five research engines, the researchers discovered a difference in flood vulnerability assessment tools and techniques that can be bridged by integrating high-resolution data with a multidimensional vulnerability methodology. The study reviewed several vulnerability components and directly examined the shortcomings in flood vulnerability approaches at different levels. The research contributed that the indicator-based approach gives a better understanding of vulnerability assessment. The geographic information system provides an effective environment for mapping and precise analysis to mitigate the flood disaster.

Drought is one of the most frequent natural disasters occurring in Pakistan and has a great influence on livelihood, agriculture, and economy. The availability of long‐term high‐quality reanalysis products over Pakistan has been of great concern in recent decades. Here, we conduct drought assessment in Pakistan based on the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) at 3, 6, and 12 months timescales during 1983–2018. We use long‐term in situ observations to evaluate the accuracy of reanalysis products, including Climatic Research Unit (CRU TS), National Centers for Environmental Prediction version II (NCEP‐2), European Centre for Medium‐Range Weather Forecasts Version‐5 (ERA‐5), and Modern‐Era Retrospective analysis for Research and Applications version II (MERRA‐2). The main results are summarized as follows: (a) drought indices and drought areas assessed from reanalysis products are relatively more representative of historical droughts that had occurred in southern Pakistan and overestimation is evident for drought severity in western than eastern Pakistan; (b) statistically significant increasing trends (1984–1998 and 2000–2010) in monthly drought areas and occurrence are evident by CRU TS and MERRA‐2 in dominant arid and semiarid regions; (c) climate variables and drought features of southern Pakistan are best represented by CRU TS and MERRA‐2, while that of southwestern and western parts are best represented by ERA‐5; (d) the Nash–Sutcliffe efficiency (NSE) results range from −2 to 1, where the NSE of SPEI values (−1.0) show relatively weaker than SPI values (0.5) in most parts of the regions, specifically in the southern Pakistan; (e) a strong positive linear relationship on a monthly scale is evident in CRU TS, MERRA‐2, and ERA‐5 exhibiting relatively high correlation coefficient (0.84), except for NCEP‐2. Furthermore, the SPEI results are found to be better than SPI; thus, this study suggests SPEI may be more suitable than SPI in monitoring droughts under climate change.

Every year, millions of people are at risk due to droughts in South Asia (SA). The likely impacts of droughts are projected to increase with global warming. This study uses the new ensemble mean of 23 global climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6), population and gross domestic product (GDP) projections to quantify future changes in increasing drought risks and associated socioeconomic exposure across SA and its subregions under 1.5°C and 2°C of warming. We used two shared socioeconomic pathways (SSPs), SSP2‐4.5 and SSP5‐8.5. The most likely realization copula functions are used to model the joint distribution of drought severity and duration. Simultaneously, changes in the bivariate return period are calculated under a warming climate. The frequency of 50‐year historical droughts (under a bivariate framework) might double across 80% of the SA land area under 1.5°C of warming. Conversely, 12% of SA landmasses may suffer extreme droughts under 2°C of warming. The severe drought episode frequency is expected to increase under 1.5°C (40%–75%) and 2°C (60%–90%) of warming relative to the recent climate. The largest exposure increase is projected in R2 and R4, then R1. Additionally, 75% (65%) of the SA population (GDP) could suffer from increased drought risks under the 1.5°C warmer climate, whereas the additional 0.5°C warming will lead to an unbearable regional situation. Limiting global warming to 1.5°C compared with 2°C can significantly reduce the drought risk influence in SA. These findings can help disaster‐risk managers to adopt climate‐smart management strategies.

In recent years, social vulnerability has gained much importance in academic studies. However, social indices are rarely combined and validated with exposure and resilience components. This study provides an integrated analysis of the flood exposure and social vulnerability of rural households in a case area of Charsadda District, Khyber Pakhtunkhwa, Pakistan. A conceptual framework was designed (based on the MOVE framework) as a guideline and key indicators were identified. For the exposure component, parameters such as elevation, flooded locations, and distance from the river were endorsed to understand flood mechanisms. For populating socioeconomic variables, questionnaire-based interviews were conducted with 210 households. The results were presented through ArcGIS-generated maps. The most significant indicators interplaying with high vulnerability were exposure-related indicators. The findings showed that the southern areas, including Agra, Daulat Pura, and Hisar Yasinzai were highly vulnerable due to having the highest number of flood locations, lowest elevations, and shortest distances from rivers, as well as larger household sizes, more elderly, children and women, illiteracy rates, and weak financial capacity. Understanding such dominant indicators and areas where high social vulnerability and high exposure converge can inform the authorities in mitigating both social and physical flood vulnerability.

The present study assessed the spatiotemporal variation of summer monsoon precipitation and its potential drivers in Myanmar, utilizing monthly precipitation data from forty‐six (46) synoptic meteorological stations spanning 1981–2020. The nonparametric statistical Mann–Kendall (MK), Sequential Mann–Kendall (SQMK) test, Empirical Orthogonal Function (EOF), and Probability Distribution Function (PDF) were used to determine the spatiotemporal monsoon precipitation trends and variability over the study period. The results show that higher precipitation occurs during June, July and August (peak monsoon period), while low precipitation was detected in May (onset month), September and October (withdrawal monsoon period), respectively. Moreover, abrupt change in precipitation is observed after 1990 with a significant (95% confidence level) increasing trend from 2000 to 2020. Decadal precipitation experienced the highest fluctuation during 2011–2020, a positive shift and increased frequency in recent decades. The spatial trends for monthly and seasonal precipitation vary from station to station and region to region due to a fluctuated shift of climatic dynamics. During dry conditions, less cloud liquid water suppressed relative humidity and high air temperature were exhibited, thus implying less precipitation in the region. However, the wet years revealed strong moisture/water vapour into the inland regions from the ocean, increased relative humidity, and suppressed air temperature. In addition, no significant relationship was found between El‐Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and monsoon onset timing with precipitation variability over Myanmar. This study provides essential information on manageable climate adaptation, mitigation and weather forecasting strategies in Myanmar.