The study aimed to systematically assess community-level risk perceptions and informal resilience capacities concerning urban fluvial hazards within Peshawar, Pakistan. The research addresses the global acceleration of urban flood hazards, a phenomenon increased by unregulated urban expansion and anthropogenic climate change. Methodologically, the study adopted a qualitative inquiry and the study was framed in an Interpretive Phenomenological Approach, utilizing the Socio-Ecological Systems (SES) framework as its theoretical construct. The SES framework operationalizes the local context by integrating the core components of risk (hazard, vulnerability, and exposure) and resilience (defined by anticipatory, adaptive, and restorative capacities) within the paradigm of the human-environment relationship. Data collection employed a multi-modal strategy including nine Focus Group Discussions (FGDs), 15 Key Informant Interviews (KIIs), and five In-Depth Interviews (IDIs), all conducted via purposive sampling. The empirical data reveal that local conceptualizations of urban flooding are primarily attributed to shifts in precipitation regimes and a spectrum of anthropogenic interventions. These interventions include uncontrolled informal settlements (encroachment), faulty urbanization, elevated groundwater tables and deficiencies in critical infrastructure, with all factors being aggravated by pervasive governance deficits. The resultant vulnerability is characterized as multidimensional vulnerabilities extending across socio-economic, physical, environmental and motivational axes. Parallel to it, communities demonstrate emergent resilience mechanisms, specifically manifesting as self-organized early warning systems and adaptive structural modifications such as elevated building plinths. The study suggests that effective urban flood risk management necessitates a paradigm shift from the silos-based top-down governance model toward a holistic, risk-informed urban planning framework. Such transition requires support from institutional reforms and formalized community engagement to effectively use indigenous knowledge and local capacities, thereby adding the system’s inherent capacity to absorb, adapt and transform in response to hydrometeorological stressors.

Urban thermal discomfort is an escalating concern, particularly in arid cities undergoing rapid urbanization and climate change. Addressing this issue is essential for enhancing urban resilience and livability in vulnerable arid environments, like the Arabian Peninsula (AP). This study leverages Google Earth Engine, remote sensing datasets, and advanced thermal indices to evaluate spatiotemporal variations in urban thermal discomfort across 13 cities of the AP from 1990 to 2024. The results indicate that although many cities achieved a 20–40

Despite the widespread decline in near-surface wind speed (NWS), climate models still show large uncertainties in reproducing historical NWS trends and projecting future changes over the Tibetan Plateau (TP). In this study, historical NWS simulations from 22 Coupled Model Intercomparison Project Phase 6 (CMIP6) High-Resolution Model Intercomparison Project (HighResMIP) datasets, consisting of 11 paired higher-resolution (HR) and lower-resolution (LR) models, were evaluated against observations over the TP during 1979–2014. Observations show a pronounced decline in TP NWS, with an annual trend of − 0.159 m/s per decade, mainly driven by spring decreases (− 0.235 m/s per decade). Both HR and LR ensemble means reproduced the spatial distribution of NWS but substantially underestimated the declining trend, with annual trends of only − 0.024 and − 0.020 m/s per decade, respectively, indicating a widespread “stilling” bias in CMIP6 models. The relatively small difference between HR and LR simulations further suggests that increasing horizontal resolution alone provides limited improvement in reproducing historical NWS decline over the TP. Based on multiple statistical metrics, seven optimal simulations were selected to construct an optimal model ensemble mean. Future projections under Shared Socioeconomic Pathway (SSP) 5–8.5 indicate a continued NWS decline over the TP during 2015–2049, with an annual trend of approximately − 0.02 m/s per decade, accompanied by continued regional warming. Random forest analysis further suggests that the underestimation of historical NWS decline is likely related to model biases in key dynamical and thermodynamical processes, particularly snow variability and surface sensible heat flux. These findings highlight the robustness of future TP NWS decline while emphasizing that improving physical process representation may be more important than simply increasing spatial resolution for reducing persistent model biases.

Climate change intensifies global drought risk through altered precipitation, rising temperatures, and increased evaporative demand. Yet, drought assessments in Pakistan largely rely on single indices and overlook the combined effects of precipitation, temperature-driven moisture stress, and future socioeconomic exposure under SSP scenarios. To address these gaps, the present study provides a comprehensive assessment of future drought characteristics and their socioeconomic consequences in Pakistan, using high-resolution bias-corrected NEX-GDDP-CMIP6 models under four Shared Socioeconomic Pathways (i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Drought conditions and resultant socioeconomic exposure (population, gross domestic product (GDP), and cropland) were assessed utilizing the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) over a 12-mon accumulation interval for mid-century (2031–2060) and far-century (2071–2100). Results indicate strong spatial variability in projected drought patterns and socioeconomic exposure, with more pronounced changes under high-emission scenarios (SSP3-7.0 and SSP5-8.5) compared to low-emission pathways. The SPI projections indicate a moderate increase in long-duration precipitation-deficit droughts across northern Pakistan, with drought duration increasing by about 2–4 mon, whereas SPEI projections show substantially stronger warming-induced drought stress across the arid regions of southern Pakistan, with local drought duration increases exceeding 6 mon under high-emission scenarios. The central and northern Indus corridor experiences a notable rise in population and GDP exposure, with population exposure exceeding 10 million people-events and GDP exposure reaching approximately 50–100 billion USD-events by the late 21st century. Agricultural exposure increases across all scenarios, with a more pronounced expansion of hotspots in Punjab, Sindh, southern Khyber Pakhtunkhwa, and eastern Baluchistan under SSP3-7.0 and SSP5-8.5. Population exposure is primarily driven by demographic factors in the near term, while GDP exposure shows earlier and stronger sensitivity to climate-driven interactions. Overall, the findings indicate that Pakistan's drought risk is projected to increase markedly under future SSP scenarios, particularly affecting the agriculturally and economically vital Indus Basin. These results highlight the importance of integrating precipitation- and temperature-based drought metrics for robust risk assessment and emphasize the need for region-specific adaptation strategies focusing on water management, agricultural resilience, urban planning, and climate-responsive economic development.

Floods occurring 12 years apart, in 2010 and 2022, accounted for 50% of the total monetary damage in Pakistan, yet comparative assessments continue to rely primarily on aggregate damage statistics that obscure persistent vulnerabilities and human-development impacts. This study conducts a dual comparative analysis of the multisectoral flood damages from the 2010 and 2022 events in Khyber Pakhtunkhwa province, Pakistan, to evaluate whether post-2010 disaster governance reforms translated into meaningful resilience gains. First, based on commonly affected severe districts, sectoral damage data for 2010 and 2022 for districts are harmonized to a common administrative geography and normalized to enable like-for-like comparison despite boundary changes and scale differences. Spatial-sectoral overlap in flood impacts was then quantified using the Jaccard Similarity Index, selected to identify persistent versus shifting vulnerability patterns across districts and sectors. Second, the Noy-lifeyears framework was applied to translate mortality, affected population, and economic damage into a human-centric measure of lifeyears lost. Jaccard analysis revealed limited degrees of similarity in the impacts of the two floods in 2010 and 2022 (mean Jaccard Index ≈0.30), suggesting that vulnerability has not simply repeated but redistributed across the province. Despite lower structural damage in several sectors in 2022, the Noy-lifeyears framework indicates that the 2022 floods resulted in greater human development losses than the 2010 floods, with total lifeyears lost approximately 4.6 percent higher. This reconciles the apparent paradox of the 2022 floods being structurally less severe yet socially more damaging. The findings demonstrate a critical resilience gap between infrastructure protection and human wellbeing. The study advances comparative disaster assessment beyond conventional loss accounting and provides evidence to support people-centered flood risk governance.

Qatar faces rising temperatures and more frequent extreme heat waves (HWs), highlighting its vulnerability to climate change. This study presents a comprehensive assessment of HW characteristics over Qatar, associated mechanisms, and land-atmosphere feedback during 2003-2024. HWs are identified using the 95th percentile of daily maximum temperature lasting at least three consecutive days. A total of 32 HW events are detected at Al Udeid station, mostly between May and September, with a gradual increase of 0.1 events per year. HWs occur under cloud-free conditions (OLR anomalies 0-8 W/m²) and weak surface pressure anomalies (~-1.25 hPa) relative to the 2003-2024 climatology, forming a strong vertically coupled heat dome from the surface to 900 hPa. This structure is sustained by upper-level subsidence, a mid-tropospheric inversion, and weak surface updrafts. Land-atmosphere interactions further intensify HWs through enhanced sensible heat flux, reduced latent heat flux, and low soil moisture, limiting evaporative cooling. Suppressed moisture transport, dry boundary-layer conditions, and widespread subsidence create a thermally stratified and dynamically stable atmosphere, reinforced by anticyclonic vorticity anomalies. These insights enhance understanding of extreme heat dynamics in arid regions and offer valuable inputs for improving HW prediction and climate resilience strategies in Qatar, specifically, and the Arabian Peninsula in general.

Eastern Hindu Kush (EHK) is one of the most flood-prone regions due to its diverse topographic features, complex climatic conditions, and fragile socioeconomic situations. Yet there are limited studies on robust assessment and prediction of flood susceptibility in this region. This study aims to predict flood susceptibility hotspots, identify significant flood predictors, and evaluate the models' performance in the transboundary Kabul River Basin (KRB) of the EHK region. The study employs a set of six Supervised Machine Learning (SML) models, namely Logistic Regression (LR), Artificial Neural Network (ANN), eXtreme Gradient Boost (XGBoost), Random Forest (RF), K-Nearest Neighbors (KNN), and Na & iuml;ve Bayes (NB), along with sixteen topographical, hydrological, vegetational, and environmental predictors and a flood inventory of 570 flooded and non-flooded locations each. Among the selected SML models, XGBoost demonstrated the highest performance, followed by RF, outperforming the rest of the SML models. The outcomes of the LR, ANN, XGBoost, RF, KNN, and NB models indicate that southern, southeastern, and stream-adjacent regions are susceptible to flooding. These findings provide a robust prediction of flood susceptibility in the transboundary KRB. This can help the relevant authorities strengthen the existing early warning systems, implement mitigation strategies, and foster community resilience.

Climate change has increased the frequency and intensity of extreme events and the occurrence of multiple extreme events simultaneously, which can have potentially catastrophic consequences. This study evaluated the effects of climate change on compound events characterized by both droughts and heatwaves (CDH), which can have severe consequences for humans and the environment, especially in arid regions. It evaluated the historical changes and projected shifts in CDH and their impact on population and agriculture in the Middle East and North Africa (MENA) area for the Paris Agreement scenarios. The quantile mapping reduces biases in monthly rainfall, potential evapotranspiration, and daily maximum temperature data from eight global climate models that simulated the SSP1-1.9 and SSP1-2.6 scenarios (goals of Paris Agreement), using ERA5 data as a reference. The results showed that the CDH duration events are projected to rise by 7 (12) days in SSP1-1.9 (SSP1-2.6) in the Arabian Peninsula and south of the Sahara Desert. The severity and magnitude of CDH events for three-month droughts (SPEI-3) are projected to increase more than other timescale droughts in the future. CDH events for SPEI-3 are expected, by the end of the century, to reach an increase of 0.5 °C and 0.7 °C, compared to 0.3 °C and 0.4 °C, respectively, in the reference period. Increased severity and magnitude of CDH for SPEI-3 would affect 500,000 km2 of agricultural land by 2060. The study suggests that even a minor temperature shift could significantly change CDH and severely affect agriculture in the MENA region.

Extreme climatic events, such as floods, are becoming increasingly frequent and severe worldwide, including in Pakistan. The Swat River Catchment (SRC), located in the eastern Hindukush region of Pakistan, is highly susceptible to flooding due to its unique geographical and climatic conditions. However, despite the region’s susceptibility, comprehensive flood risk assessments that integrate hazard, vulnerability, and exposure components remain limited. To address this gap, this study assesses flood risk in the SRC using 22 indicators distributed across the three core dimensions of flood risk: hazard, vulnerability, and exposure. Flood hazard was modeled using 11 indicators, broadly categorized into environmental, hydrological, and geographical aspects, while vulnerability was evaluated through socio-economic factors, geographical proximity, and land use characteristics. Exposure was analyzed based on population metrics and critical infrastructure. All data were converted into thematic layers in GIS, systematically weighted using the Analytical Hierarchy Process (AHP) and combined to produce hazard, vulnerability, and exposure maps respectively. These maps were then integrated through a risk equation to generate the final flood risk map. The results reveal that 31% of the study area is in a high flood risk zone, 27% in moderate risk zones, 23% in low risk, and 19% are safe areas. The results were validated using the Area Under the Curve (AUC) technique, yielding a value of 0.92, which indicates high reliability. By presenting the first integrated flood risk assessment for the SRC, this study provides valuable insights into flood-prone areas and risk distribution. These results highlight the urgent need for enhanced flood risk management, especially in urban areas. The developed methodology serves as a valuable tool for disaster management authorities and planners, helping them make risk-informed decisions, allocate resources efficiently, and implement targeted flood mitigation strategies.

The Paris Agreement stipulates that nations must reach a peak in global carbon emissions and achieve carbon neutrality by mid-century to prevent anthropogenic global warming from exceeding perilous thresholds. Recently, much effort has been made to assess future changes in heat stress and related risks in the Arabian Peninsula (AP); however, a significant knowledge gap remains in accurately quantifying the heat-risk mitigation benefits of carbon-neutral policies, primarily due to inherent limitations in conventional climate simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). In this study, we used multi-model large ensemble simulations from the CovidMIP-a CMIP6-endorsed intercomparison project-to quantify the avoidable heat risks in AP during 2030-2049 under two distinct global carbon-neutral scenarios: a moderate green recovery (MOD) scenario by 2060 and a strong green recovery (STR) scenario by 2050, relative to a fossil-fueled baseline (FOS) scenario. The findings indicate that both STR and MOD scenarios offer substantial benefits by mitigating significant heat stress (0.28-0.48 degrees C and 0.06-0.22 degrees C), heat stress days (12-30 days and 2-10 days), urban population exposure (30% and 21%), and heat-related mortality risk (3.5% and 1.4%), respectively. Moreover, an additional 0.06-0.20 degrees C of heat stress, 2-10 heat stress days, 10% of urban population exposure, and a 2.1% reduction in heat-related mortality risk could be avoided if carbon neutrality is attained a decade earlier. These projected benefits are particularly pronounced in major urban centers of the AP, underscoring the need for the region's governments to implement ambitious carbon mitigation policies to mitigate heat risk in cities.

Urbanization and climate change are intricately linked, significantly influencing local and regional thermal environments. Kolkata, a rapidly expanding metropolitan city in India, has witnessed substantial shifts in urban thermal dynamics due to increasing land surface temperatures (LST), the urban heat island (UHI) effect, and heightened thermal discomfort. This article integrates high-resolution remote sensing data and a cloud-based platform via Google Earth Engine to assess spatiotemporal changes in thermal discomfort in Kolkata. The article employs the urban thermal field variance index (UTFVI) to estimate the urban thermal discomfort, while the Sen's slope estimator and the modified Mann-Kendall tests are applied to assess long-term spatiotemporal trends in urban thermal conditions. Findings reveal that LST, UHI, and UTFVI are significantly increasing at the rates of 0.149 degrees C/year, 0.041 degrees C/year, and 0.0041 degrees C/year, respectively. The results further reveal that those areas with low vegetation cover experience extreme thermal stress, highlighting the critical role of urban greenery in mitigating heat-related discomfort. The article offers data-driven insights into Kolkata's urban thermal landscape, with guidance for policymakers in developing sustainable urban planning and climate adaptation strategies, such as expanding green spaces, implementing cool roof technologies, and enhancing urban ventilation. By leveraging cloud-based remote sensing, this article provides a scalable framework for assessing and addressing urban thermal discomfort in other rapidly urbanizing cities worldwide.

Assessing multi-hazard susceptibility and understanding community insights are important for effective disaster risk management; however, limited research has been conducted to study these aspects together. This study uses a data-driven approach to assess multi-hazard susceptibility and community perceptions, aiming to deepen climate change mitigation strategies. We employed a two-stage framework in Eastern Hindukush, Pakistan, which is based on machine learning, remote sensing, geographical information systems, and index-based methods. In the first stage, flood and landslide inventories were generated, and predictive factors were analyzed using logistic regression, resulting in an integrated multi-hazard susceptibility map. In the second stage, a survey of 410 household heads assessed community risk perception, communication, and preparedness, using a structured questionnaire with 28 Likert-scale indicators, and a composite index was calculated. The findings indicate that 25.81 % and 35.43 % of the study area are susceptible to flooding and landslides, respectively, with 15.07 % vulnerable to both hazards concurrently. On the other hand, the community is generally aware of flood and landslide risks; however, there are significant gaps in coping abilities and preparedness, including insufficient insurance coverage and training. Moreover, socioeconomic challenges, such as limited access to information and low trust in local authorities, further complicate disaster preparedness efforts. This study provides a holistic framework for identifying multi-hazard hotspots and assessing community perceptions, facilitating targeted interventions to enhance disaster preparedness and resilience in the region.

Global climate models (GCMs) are used to assess historical, current, and future climate change worldwide, including Pakistan. The NASA Earth Exchange Downscaled Projections Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) provides detailed spatiotemporal climate data. This study evaluates NEX-GDDP-CMIP6 models in simulating historical (1951–2014) and future (2015–2100) temperatures projections of minimum (Tmin), maximum (Tmax), and mean (Tmean) for Pakistan under four Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Historical temperatures simulation was assessed using Taylor diagram and interannual variability skill (IVS) which were chosen for their ability to comprehensively capture both spatial correlation and temporal variability. Additionally, a Comprehensive Ranking Index (CRI) was employed to rank model performance in temperature distribution. The MPI-ESM1-2-HR model ranked highest for temperature distribution accuracy, followed by CanESM5, CNRM-ESM2-1, IPSL-CM6A-LR, GISS-E2-1-G, UKESM1-0-LL, TaiESM1, MIRCO-ES2l, MRI-ESM2-0, and EC-Earth3. The top models were ensembled for future projections, indicating substantial warming under all SSPs, with the highest increase under SSP5-8.5 (6.0–8.0 °C), followed by SSP3-7.0 (5.0–6.0 °C), SSP2-4.5 (4.0–5.0 °C), and SSP1-2.6 (2.0–4.0 °C) by the end of the century. Northern and western mountainous areas are projected to warm more significantly (1.0–2.0 °C near-term, 2.0–4.0 °C mid-term, 3.0–7.0 °C long-term) than low-lying plains. These findings guide GCM selection in data-scarce regions and highlight the need for mitigation, adaptation, and resource management strategies in response to projected temperature increases, which could impact agriculture, water resources, and disaster risk.

Flood risk assessment is crucial for effective disaster risk management and community resilience. However, the current research lacks strength in identifying high-risk areas, implementing flood early warning systems, prioritising risk reduction measures, and allocating resources for emergency response planning and management. This study aims to assess flood hazard in Mirzadhare, Charsadda a highly flood-prone area in Khyber Pakhtunkhwa province of Pakistan. The study used an integrated approach by employing geographical information system (GIS) and multi-criteria decision analysis (MCDA) techniques. Further, the study used multiple datasets, including rainfall, stream density, and village points to map out flood susceptibility in the study region. Data was collected from field surveys, questionnaires, and interviews, allowing for a detailed analysis of flood hazards. Selecting average precipitation, peak river flow, and historical flood frequency as indicators, the weights of the three are 0.4, 0.3, and 0.3 respectively, an indicator system for predicting flood disasters was constructed. The results categorised the study area into four hazard zones: very high, high, medium, and low, based on their susceptibility to flood hazards. The study findings reveal that more than 65% of the area, including agricultural land with other livelihood settlements, is at a very high risk of flood hazard. Over 50% of the population lives in floodplains and faces an extremely high risk of future flood events. The precision of the results may have been affected by the accuracy and completeness of the data sources utilised, such as historical flood records, precipitation data, stream network data, and stream density. This combination of methods enabled the creation of accurate, data-driven flood risk maps. The hazard map of the area serves as a valuable tool for decision-making, resource allocation, and the development of flood risk management strategies. Based on the study findings, regular updates and continuous monitoring are recommended to ensure the accuracy and relevance of the flood hazard information over time.

Summer is ideal for outdoor activities like walking; however, as extreme heat rises, these pleasant walks may gradually be overtaken by heat stress, compromising pedestrians' comfort and safety at risk. This study investigates the implications of climate change on pedestrian thermal comfort by estimating future changes in thermal discomfort days and their potential impacts on walkability across Saudi Arabia and its major cities. The study uses the outputs of 27 bias-corrected high-resolution models from NASA's NEX-GDDP-CMIP6 program to estimate projected changes in discomfort days with different thermal stress ranges during the near-future (2021-2040), mid-future (2041-2060), and far-future (2081-2100) periods under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The study also estimates future urban population exposure to discomfort days in Saudi Arabia under the selected SSP scenarios. Future projections under various SSPs indicate a significant increase in very uncomfortable days across most parts of Saudi Arabia, with impacts intensifying inland by the end of the 21st century. Coastal regions, though expected to experience fewer uncomfortable days, will see an increase in days falling into higher discomfort ranges. Under the SSP5-8.5 scenario, the projected rise in extremely uncomfortable days, particularly across coastal and inland areas of Saudi Arabia, is predicted to substantially affect walkability and limit outdoor activity. In terms of urban environments, Jeddah, Dammam, and Madinah are projected to experience the highest levels of discomfort, reaching up to 35 degrees C under the SSP5-8.5 scenario by 2100. The projected urban population exposure is likely to be 1-8 million people-days in Saudi Arabia, with coastal cities experiencing the highest exposure in the future periods, particularly under high-emission scenarios. Our findings emphasize the critical need for adaptive urban planning to ensure outdoor spaces remain accessible and comfortable for pedestrians in a warming climate.

Comparison of Coupled Model Intercomparison Project Phase 6 (CMIP6) General Circulation Models (GCMs) with observations under different climatic conditions is necessary to determine their respective strengths and differences. In the current study, ten CMIP6 GCMs are compared with measured gauge precipitation data of 51 stations across Pakistan. Results show reasonable agreement between the CMIP6-GCMs with measured data in capturing precipitation days of ≤10 mm/day. The precipitation intensity of events ≥10 mm/day shows a significant resemblance with measured data at a 95% confidence level (K-S test). Furthermore, the results of regional differences demonstrate the relatively good agreement of CMCC-CM2-SR5, EC-Earth3-AerChem, and EC-Earth3-CC with measured data in arid and semiarid regions and FGOALS-f3-L in humid and extremely arid regions. Significant precipitation variability is reported in the interannual standard deviation ratio (STD) for all GCMs in all seasons, implying more dynamics and intense precipitation in GCMs. The magnitude of STD is sensitive to the precipitation magnitude in time and space rather than climate classes, higher and lower in monsoon and autumn seasons, respectively. The climatological mean shows higher precipitation in the northeastern and southeastern parts of GCMs during the monsoon and lower precipitation during winters complementing station data. Based on selected metrics, CMCC-ESM2 has the highest skill in simulating precipitation distributions over Pakistan, followed by CMCC-CM2-SR5 and EC-Earth3-CC, while NorCPM1 ranked the worst in reproducing measured precipitation. The findings can serve as a benchmark in the region for applying the CMIP6 GCMs in water and food security studies.

Pakistan’s vulnerability to disasters necessitates effective disaster risk communication. This study presents a conceptual model of the PDMA Madadgar Application (hereinafter Madadgar) for subsequent code development and testing. Employing the design science research approach, data were collected through in-depth interviews from the purposefully selected sample participants and analyzed through the content analysis method. Our findings highlight the conceptualization of the app and the strengths it provides in real-time disaster alerts, early warnings and critical information dissemination. The data reveals that the model is highly interactive. A major stake has been provided to the local communities and field-based staff to receive and disseminate early warning messages, locate evacuation centers, report disasters without warning, and digitally conduct damage assessment. This study enhances disaster risk communication in Pakistan and informs the global development of effective mobile-based solutions. Maddagar is Pakistan’s pioneer interactive Android-based disaster risk communication app for communities in Pakistan. Madadgar directly contributes to the local implementation of Pakistan’s National Disaster Management Act 2010 and National Disaster Risk Reduction Policy-2013 as well as the Sendai Framework for Disaster Risk Reduction and the Sustainable Development Goals. While the current Madadgar model is specifically designed for use within the Khyber Pakhtunkhwa province of Pakistan, reflecting the decentralization of disaster risk reduction to the provinces following the 18th constitutional amendment, its underlying principles and architecture offer a scalable blueprint for adaptation and replication in other provinces and similar contexts.

This study investigates projected changes in heat stress in a changing climate and their impacts on population exposure, work performance, and mortality in the Arabian Peninsula (AP). The findings demonstrate an intensification in future heat stress across the AP, with a projected increase in extreme caution and danger days under SSP5-8.5, compared to SSP2-4.5 and SSP1-2.6 scenarios. The coastal cities are projected to experience higher heat stress intensity and more heat stress days than inland cities, making them more vulnerable to the impacts of climate change. By 2100, the average population exposure to extreme caution days will increase by 1.38–2.5 and danger days by 0.13–1.0 million people-days under the selected SSPs, relative to the baseline period. Coastal regions are particularly vulnerable, with work performance potentially declining by 6%–21%. The heat-related mortality risk ratio will increase by 2.3–35.4 times under the selected SSPs across the entire AP. The implications of these findings might influence the response and mitigation strategies, highlighting the critical necessity for focused policy initiatives aimed at reducing emissions and improving adaptive capacity in the region to address climate change.

Study region: The Kabul River Basin (KRB), located in the eastern Hindukush region, is a transboundary basin shared by Afghanistan and Pakistan. Study focus: This study aims to identify and map hotspots for five major flood types: ephemeral, fluvial, flash, urban, and glacial lake outburst floods (GLOFs) using three models: the Frequency Ratio (FR), Expert Opinion-based Analytical Hierarchy Process (EO-AHP), and Prediction Rate-based Analytical Hierarchy Process (PR-AHP). By analyzing 620 flood locations and 18 flood predictors, we found that 14 %, 20 %, and 15 % of the study area derived from FR, EO-AHP, and PR-AHP models were highly susceptible to multi-type floods, respectively, highlighting susceptible hotspots of different floods in the KRB. New hydrological insights for the region: This study provides a comprehensive multi-type flood susceptibility map for the KRB. The findings of this study are crucial for effective flood management, as they identify and integrate multi-type floods into a single map. The study helps relevant authorities in prioritizing resource allocation, improving early warning systems, and implementing sustainable land use planning, ultimately improving flood preparedness and building resilience through risk-informed policy-making.

The urban walking experience is undergoing profound challenges as it grapples with increased threats of climate change. Effectively understanding walkability in this context requires a detailed examination of how climatic and weather extremes disrupt outdoor walking. Here the authors offer insights into the nexus between climate change and walkability, emphasizing how this connection can be examined through the lens of thermal comfort in urban environments. This nexus is explored by examining various methodologies and climatic zones to evaluate how various weather conditions—particularly extreme heat—affect walkability in cities. The assessment of thermal comfort, adaptation strategies and relevant indices enables a deeper understanding of how outdoor thermal stress impedes walkability. Overall, the walkability‒climate change nexus not only reveals the challenges to walkability, but also presents opportunities to explore climate adaptation strategies that enhance the urban pedestrian experience. Climate change is making walking in cities more difficult. This Review examines the connection between climate change and walkability, focusing on thermal comfort in complex urban environments.